Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result. Identifying the tumors and abnormalities in the human body through medical imaging for investigative analysis has become a regular practice. Various medical imaging technologies are used for diagnosis depending on the type of disease and the body part. The imaging must be performed for an early and precise abnormality diagnosis. The technologies include X-ray, which is used in the identification of bone fractures or for the identification of hard tissues , and comMRI is most widely used in computer-aided diagnosis because of its low radiation and noninvasive nature. On the one hand, MRI can generate a more detailed report of soft tissues compared to CT scans. On the other hand, MRI technology can capture multiplaner images that can capture multiple angles in different planes. Moreover, MRI is capable of producing angiographic images without the use of any contrast material. The patient can be periodically investigated through MR imaging. The assessments of CAD models are being analyzed to assess the tumor's impact, which would assist in deciding the influence of the drug that acts on the abnormality. The current study identifies the tumor's progress as actual, enhanced, and whole tumors .  FigureThe primary goal of the current study is to mechanize a self-learning model that can make a precise assessment of tumor progression with minimal dataWe discuss various existing tumor progression assessment models based on image segmentation and their limitationsThe autoencoders generate numerous images identical to the original MRI images for trainingIntegrating the LSTM component with the self-learning model improves performance by maintaining adequate model parametersUsing the grey-level co-occurrence matrix for the feature engineering task by the self-learning network model that assesses the number of occurrences of combinations of the pixels found in the input MRI imageAnalyzing the hyperparameters to determine the efficiency of the self-learning modelAnalyzing the performance of the proposed model against benchmark evaluation metrics like sensitivity, specificity, accuracy, and F1-scoreAnalyzing the tumor impact through measures like tumor core, enhanced tumor, and whole tumorThe existing state of art models that work over the segmentation procedures could only recognize the region of the tumor and they are not efficient in classifying the type of tumor and the progress of the tumor growth . The othThe paper is structured as follows: the introduction presents the domain of the study and the author's contributions. Various image segmentation approaches are available to localize the abnormality from the MRI images. The K-means-based segmentation algorithm is among the approaches that are most extensively used in semiautomated segmentation mechanisms that need the number of segments to be predetermined . MoreoveFuzzy c-means  is the iThe MRI images are being segmented using fully automated mechanisms such as thresholding , region The watershed-based image segmentation model works based on edge-based information that necessitates foreground and background planes as separated files that require high computational resources for preprocessing the image . The watThe pixel label-based image segmentation  is a robA probabilistic neural network (PNN) , in conjArtificial neural networks. (ANN) is a predominantly used segmentation approach for medical imaging technology, motivated by the biological neurons being practically implemented through artificial neurons controlled by the activation and transfer functions. Based on the underlying ground facts and working principles, the auto-encoders are designed in divergent architectures that can either adhere to the logic of a recurrent neural network (RNN) , which hDeep learning-based image segmentation is among the most widely used image segmentation models in recent years that would yield better accuracy in tumor identification . The accThe proposed SLNS-LSTM technique incorporates several components integrated for performing the automated segmentation and assessing the tumor's progress. The images are preprocessed internally to discard the noise, and then the images are segmented based on texture-based information. The information related to the previous experiment is loaded in a temporary memory, namely, the long short-term memory, which is widely used with recurrent neural networks. In image segmentation, feature selection is significant in attaining remarkable accuracy.The self-learning algorithm is technically efficient in addressing the segmentation of novel cases, and the algorithm will train itself to segment every case. Initially, the algorithm is partially trained with the available data set, and every time segmenting an image, the algorithm is self-trained. The algorithm will have an auxiliary memory to maintain the historical information of previous executions. The algorithm is sturdy to handle both noisy and noiseless images. The machine is trained with the noisy image at an acceptable level using the labeling approach  through In automated segmentation for the abnormality diagnosis, it is crucial to distinguish brain tissues from nonbrain tissues. To be normalized, the original MRI image comprises much nonbrain tissue, such as the skull region, brainstem, thalamus, and brain fluid. The proposed algorithm is a layered approach for discarding all such nonbrain tissues for better statistical image analysis. An explicit algorithm is being executed in all the conventional mechanisms simultaneously to discard the nonbrain tissue that needs additional effort to perform the task. The self-learning component is among the multiple layers in the proposed algorithm, to which the outcomes of the previous executions are fed as the input. The progress of tumor growth is assessed by the area of the abnormality that has increased from the previous scan for a particular patient. The texture-based information is being considered in evaluating the progress of the tumor.pα=pα(m|n)pα(n) concerning the parameter α and posterior qβ(n|m) concerning the parameter β. The prior images are used to sample the latent variable n, i.e., n  ~  pα(n), while the observation model is used to sample the observation variable m, i.e., m ~ pβ(m|n). The optimized probability concerning the original images x is shown in the following equation:Dealing with the self-learning models, the models are desired to work with smaller datasets. On splitting the datasets as the training and testing partitions, the size of the data meant for training may not be adequate, resulting in the underfitting of the model. The variational autoencoders are used in the current study to generate images identical to the training images. The variational autoencoders consist of two components, namely, the encoder and the decoder. The encoder maps the original picture to a latent space and reconstructs the information in the latent space back into its initial dimensions performed by the decoder. The variational autoencoders (VAE) are probabilistic in assessing the process of feature assessment. VAE gives a probabilistic description of an observation in latent space. Thus, rather than developing an encoder that produces a single value to represent each latent state feature, it is preferable to use many values. VAE presents the probabilistic distribution for every attribute in the latent space. The probabilistic measure is associated with the joint model pα(m|x) is accessible, the posterior is evaluated using the qβ ~ pα. The conditional evident lower bound is assessed using Jensen's inequality, as shown in the following equation:Equation  correspoThe images generated using the variational autoencoders are used in training the self-learning network. The apparent loss function of VAEs, comprising a rebuilding component and a regularization element, may be precisely constructed using the statistical approach of variational inference; thus, the name variational autoencoders, is given a basic probabilistic model that represents the data.Long- and short-term memory has significantly impacted identifying the tumor from the MRI image. LSTM comprises two classes that are categorized as tumors and nontumor tissues. Whenever the image is fed as the machine's input, it classifies the tissues based on the antecedent experimental studies . The proquations  and 5)..ff⟶ and σ represents the sigmoid function,  Wi denotes the weighted matrix, bias means the bias for both feed-forward represented by t represents the each input pixel sequence represented by pix = .From the above equation, the variable equation  and feedequation , and theSelf-learning network-based segmentation (SLNS) is a layered approach comprising several convolutional layers that carry out the various crucial tasks in the procedure of automated segmentation that involves the preprocessing for the unwanted noise removal from the image using the adaptive fuzzy contourlet transforms . Then, tSLNS approach-based MRI image segmentation requires partial training from various sources that involves acquiring the data related to the segmentation of the images from the labeled segmentation datasets and the knowledge that the algorithms have learned from the previous experimentation. In the proposed approach, every layer of the network is significant, one of the layers performs noise removal through adaptive fuzzy contourlet transforms, and the rest of the sublayers would evaluate the optimal number of segments and appropriate segment centroid, respectively, that are refined for several iterations until it reaches an optimal solution.The original image is being processed by the noise removal layer of the proposed SLNS network using an adaptive fuzzy counter transform mechanism that enriches the image quality from magnitude and anisotropy perspectives , 43. TheThe LSTM memory module is integrated with the layered architecture of the self-learning network model, which holds the layers like the pooling layer and the convolution layers of the proposed architecture. The pooling layer's responsibilities are incorporated with the convolution layer that assists in incrementally decreasing the spatial dimensions for the depiction, minimizing the features needed for a thorough reorganization, and assessing the tumor from the MRI image. The convolution layer performs in decomposing the image to the maximum possible extent for approximating the tumor by using the properties that are being recognized. The convolution layer has a significant impact on the computation latency and the accuracy of the algorithm. The technical tradeoff over the convolution layer is the down-sampling performed in the striding at the convolution layer. The more it decomposes, the more the accuracy of the classification. Still, there would be a considerable burden on the algorithm that can affect the computational latency of the model. In the proposed model, the LSTM component is integrated with the segmentation component of the proposed model; the LSTM model will maintain the information about the segmentation outcomes of the previous experimental instances. The LSTM and training set information are used in the classification process. The MRI images are then classified as tumor and nontumor images for diagnosing the tumor.The self-learning model's layered approach comprises the convolution layer with the various kernels applied to the input MRI image to recognize the feature maps that will assist in the classification process. Then, the resultant outcome is processed using the kernel through the HARIS algorithm. The tensors of the second convolution layer are applied with the Max Pooling layer that down-sampling the input data for faster processing. The LSTM component handles the vanishing gradient problem of dealing with high-dimensional images. Incorporating the LSTM is a recognized dependency among the features in determining the abnormality regions in the segmentation process. The fully connected layer in the SNLS-LSTM architecture will map the features organized by the nonlinear associations in one layer with the activation unit in the upcoming layer. The SoftMax layer will assess the probabilistic measure of the image classified among the tumor and nontumor regions. The noise is predicted using the hybrid kernel to discard unwanted noise. The equation for noise analysis is as follows:pix_int denotes the corresponding Gaussian kernel coefficient associated with coordinates , and the function ω indicates standard deviation concerning the dimensional coordinates . The variable fz is the fuzzier arbitrary value that is determined by the pixel membership following the image segment, whose value is defined as follows:From equation , the varfz(pixi) determines the pixel's fuzziness concerning the segment. and the variables timax, kimax and uimax are the antecedent augments. pixi is the ith pixel in the input image.From the above equation, the variable p, q), the key value represents the frequency of occurrence horizontally adjacent to the corresponding pixel intensity p. If an image of 228 bits is considered, the GLCM matrix would be 228 × 228. The coefficient of the GCLM for the square size image is approximated through the following equation for an MRI image with “p” rows and “q” columns:Feature selection is one of the pivotal phases of image segmentation, as the performance of the segmentation algorithm depends on the features through which the image is being segmented. For feature selection, the grey-level co-occurrence matrix (GLCM) is very suitable for identifying the features from the MRI images . GLCM isdp, dq):By conducting normalization on the coefficients of GLCM, they are represented by probability instead of frequency of occurrences. The terms are further divided by the entire list of possible combinations. The same has been presented in the following equation with a displacement vector  mechanism. The arbitrary variables μ1, μ2 The above equation would approximate the average intensities of the pixels in the corresponding segment.Equation  is expanμclstumor, μclsnon_tumor It would denote the evaluated fuzzy membership concerning the image segment corresponding to the tumor and nontumor classes.A fuzzy entropy-based thresholding mechanism would assess the pixel's correlation concerning a segment in the image that tells to what degree the pixel belongs to the region. The fuzzy entropy threshold is determined using Shannon's entropy, whose values would always like within the range of 0 and 255 for any given grayscale image defined in equation , the varThe value assessed for the intraclass correlation, as stated in equation , would dσseg denotes the standard deviation among the pixels concerning the corresponding segment computed locally, and the component σimg indicates the standard deviation of the entire image that is calculated globally.From equation , the comIi, which are assessed following the likelihood ratio of either class of tissues identified from 2D MR images of tumor(i)/nontumor(i).In working with Long- and short-term memory, the brain tissues are classified as tumors. Nontumor tissue classes from the previous execution and the classes are assigned weights based on the equations  and 16)16) that The labeled data for the partial training of the self-learning model is obtained from the BRATS 2015  open-souThe heuristic approach of real-time image segmentation (HARIS) has been incorporated for better performance and accuracy . The algtumor regions. The working procedure of the HARIS algorithm is presented in this section, and the libraries and environment settings used in the real-time implementation of the mechanism are presented.The heuristic approach for real-time image segmentation technique is a multiobjective, function-based automated image segmentation algorithm that can automatically segment an image with reasonable computational efforts compared to its counterparts. The initial object function segments the medical MRI image, and the later objective function refines the segmented image, which continues for a few iterations until the stopping criteria are reached. Initially, the algorithm approximates the number of segments at the beginning through the elbow approach rather than choosing random segments like any other approach. The initial count of segments is determined through the following equation:seg Based on the ground facts, the value approximates 23 in the beginning iteration. In equation  designates the distance measured between the pixel and the corresponding image segment. In assessing the distance measure, the Mahalanobis distance measure is used to assess the mean of the feature vector, which is approximated from the mean value of intensities in the corresponding segment. The Mahalanobis distance d2 is assessed for the pixel pixn with the intensity, int is approximated from the following equation:From the above equation , segcentDx−1 denotes the inverse of the matrix that has been produced from the covariance matrix in the following equation:In the above equation, intThe individual in each segment with concern to the segment centroid is determined from the following equation:The objective function-I performs the image's task by approximating the random segments through the elbow approach and assigning the pixels to it by evaluating the membership of the pixel to the corresponding segment. The pixel likelihood to the corresponding segmentation is assessed concerning the centroid of the segment that is evaluated from the following equation:Cr, and the range of r lies between 1 and max. The objective function for estimating the pixel allotment is evaluated using the following equation:In the above equation, the corresponding segment centroid is identified by the variable α, β are the two arbitrary variables that decide the accuracy and performance of the HARIS algorithm. The variable α determines the interclass variance, and the variable β determines the intraclass correlation of the pixels concerning the centroid of the segment. totpix designates the sum of pixels, pixr denotes the sum of pixels in the region r of the image and totcent designates the sum of segments in the image and pixs designates the number of pixels in the region s. The values of two arbitrary variables that are illustrated as the deciding factors for the performance of the algorithm are defined through the following equations:In the above equation, the variables α determines the intraclass correlation, which figures out how closely the pixels are close to each other in concern to the centroid of the pixel, and the variable β in equation (In the above equation , the varequation  determinequation , and theObjective function-II is to fine-tune the resultant outcome of objective function-I. The major focus of the current objective function is to identify the optimal segment centroid of the global best solution. All the membership function assigns all the pixels corresponding to the segment centroid based on the objection function and is assessed through the following equation:i designates the current evaluation of the fitness of the seed point of the segment, and the variable fitness_spi−1 designates the fitness of the seed point in the previous iteration.  GBfitnesssp denotes the segment seed point's fitness value considered the global best in fitness, and the values are assessed iteratively.From the above equation, spThe following is the environment set up for the practical implementation of the algorithm. The experimentation has been conducted on several input images for a precise assessment of the accuracy of the proposed system. The SLNS-LSTM technique has been executed over the medical MRI images obtained from online repositories. Moreover, the experimentation is performed on MRI images of variable size. It is observed that the algorithm has exhibited better performance for smaller-size images than other algorithms. The self-learning approach is being evaluated through various benchmarks, including sensitivity, specificity, accuracy, and F1- Score. On the one hand, the experimental values of true positive values determine how well the self-learning approach correctly recognizes the abnormality.Moreover, the true negative determines how well the proposed method accurately identifies the nontumor areas in the human brain. The false-positive rate determines how often the proposed approach fails to recognize the tumor region correctly. Furthermore, the false-negative rate determines how often the proposed method fails to recognize the nontumor region correctly. Based on TP, TN, FP, and FN values are used to assess the values on the experimentation of the proposed approach for several rounds, and the accuracy is assessed accordingly.The proposed self-learning model is initially trained with 20% of the dataset with the labeled data, and the model is designed to learn from the experimental outcome consistently. The SLNS model would exhibit higher learning factors at initial epochs and lesser at later stages as the model would have gained sufficient knowledge for abnormality identification. The learning rate of the model is presented in The other hyperparameters, such as the training and validation loss and the training and validation accuracies, determine the model's performance concerning the underfitting and overfitting situations in training the model. Underfitting happens when a machine does not learn from the data and does not generalize effectively across the validation data. The training accuracy plot could be flatter or have significant loss values, indicating that the model did not learn the training samples. Overfitting occurs when a model has learned excessively from the training samples, resulting in unpredictability while evaluating the validation data. The training loss plot falls as the number of epochs increases, and the validating loss plot drops to a level before rising. The corresponding graphs of loss and accuracy measures are shown in The experimental outcome of the SLNS-LSTM techniques is compared with its counterparts, and the tabulated values are presented in The computational latency of the proposed approach is assessed through the execution time. The proposed approach has better performance when compared to others. However, there is a technical tradeoff, such as execution time. The execution time of the proposed approach is slightly more than that of the HARIS algorithm. Nevertheless, the proposed approach has exhibited better accuracy for MRI images. The impact of the tumor is assessed at the tumor core, whole tumor, and enhanced tumor to identify the tumor's progress in treating the patients. Consequently, the proposed approach would effectively identify the tumor's growth and help provide better treatment to the patients. The enhanced tumor in the tumor region is suspected to be spread compared to the earlier diagnosis of the ailment of the corresponding patient.With the change in food habits and environmental conditions, there are multiple different diseases that human beings are undergoing. Consequently, new methods that include artificial intelligence and robust algorithms can work with minimal training to support medical decision-making. Moreover, approaches such as convolution neural networks and fuzzy recurrent neural networks need tremendous training in the existing cases to attain better accuracy. On the one hand, the proposed method productive can recognize the abnormality with high precision with the knowledge acquired from self-learning from the previous cases. The algorithm would keep on enriching the performance after that. In the current study, various performance contribution techniques like dropout factor and batch normalization techniques are not performed in the current study. The hyperparameters finetuning would also assist in better performance of the model. The feature selection techniques like gray-level co-occurrence matrix might be used in future models as feature engineering would have a significant impact on the performance of the model. The current models lack the above-discussed performance optimization mechanism that has resulted in an accuracy of 87.9%, which could be further improved by fine-tuning the same.On the other hand, the self-learning algorithm relies on the HARIS algorithm for automated image segmentation over multiple iterations. Moreover, long- and short-term memory is used with the recurrent neural network for handling and preserving the state information of irrational long-term dependencies for predicting the time series data. In this experiment, the proposed approach has outperformed when compared with other existing self-learning-based algorithms. By incorporating the LSTM-based approach, the algorithm would perform much faster in assessing the tumors' progress and have better accuracy in differentiating the tumors and nontumor regions compared to its counterparts.The current research examines the role of long- and short-term memory in improving the efficiency of self-learning network-based classification by feeding the network state information from an MRI picture. The insights from the pictures produced by the variational autoencoder are used as input for the tumor model's differential categorization. Furthermore, the self-learning technique can identify tumor progression with less training, resulting in quicker execution than its competitors. Self-learning models use less computer resources since they require minimum training and make the model acceptable for tumor diagnosis. The experimental results showed a decent accuracy of 89.7%. The proposed approach has outperformed other machine learning models' accuracy and execution time. The current study is confined to a limited number of samples for the training and testing purposes. The images are generated by the variational autoencoder for the training purpose. The proposed model has yielded an acceptable performance, but by optimizing the hyperparameters, the accuracy of the model can be further enhanced.The SLNS model's performance has been greatly improved by including the LSTM module. However, the suggested model's performance may be improved further by using bi-directional LSTM and self-labeling techniques. Optimizing the class weights at each iteration will result in a promising result that may be explored for future perspectives. The deep variational autoencoder produces images of comparable quality to the variational autoencoder alone. The deep variational autoencoder, on the other hand, requires more computer resources than the other standard techniques. This future direction would assist in building a robust model that could efficiently detect the progress of the brain tumor. The earlier analysis of the tumor impact would assist in providing the appropriate treatment and decision-making.


Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network. Hyperspectral imaging (HSI) systems can be utilized in various sensing applications. The highly dimensional hyperspectral (HS) data offers high accuracy and robustness for identification and characterisation tasks ,2. A HS Spectral imaging systems were originally applied in remote sensing applications, where systems are robust to rough or irregular topographies, because imaging is performed from the satellites or airplanes. When imaging is performed from a closer range, surface topography and tomography start to affect the image’s spectral quality and homogeneity. Recent advance in hyperspectral sensor imaging has made sensors smaller . Using tHS imaging is proven to be a powerful tool for detecting and identifying diseases in medical research ,6,7. In This article is the first stage of a pilot study completed in three stages from 2020 to 2022. The aim is to introduce the working principles and clinically pre-test the concept of the new, compact hand-held SICSURFIS Piezo-actuated metallic mirror FPI hyperspectral imager (SICSURFIS HSI) for complex skin surfaces. This pre-test enables the system engineering, imaging, and analysis aspects of further system development toward a clinical application.The SICSURFIS HSI is designed to face the mentioned challenges of the previous studies. For illumination and challenging skin surfaces, there is a controllable built-in light-emitting diode (LED) based illumination module, which is designed for photometric stereo imaging. The special stray light protection cones will block the unwanted light and enable the imager to reach and adapt to complex skin surfaces. The imager produces spectral, spatial and surface topography information.In the data processing, the raw measurement data were processed into the surface albedo, normals and surface model. The processing was performed with common hyperspectral reflectance calculations and a photometric stereo method for the surface model calculation . TraininOur hypothesis is that these surface models can improve the hyperspectral imaging machine learning model’s performance in clinical decision making and the new imaging concept can adapt to the complex skin surfaces. The skin surface models combined with the spectral and spatial domains will benefit the classification and delineation results of the convolutional neural network (CNN). In this first-stage pilot study, the aim is to compare the capacity of the HS in differentiating malignant basal cell carcinomas (BCCs) from clinically similar-appearing but benign intradermal nevi (ID) by comparing the surface models and albedo maps of the measured BCCs and IDs with CNN. So far, this is the pre-study for testing the concept, and evaluating the technical and instrumental aspects from the analysis point of views. The prototype imager with its application is not ready for a clinical use, but the first results and future potential can be evaluated. The mentioned second and third study stages will be independent continuation clinical pre-studies, with more lesions and lesion types. The imaging procedure, methods, and machine learning models will be improved based on these first steps.The paper is organised as follows. This section introduces the SICSURFIS HSI’s concept in six subsections; the design and operating principles, spectral responses and imaging calibration are presented in The SICSURFIS Spectral Imager (SICSURFIS HSI) was designed to provide spectral images enlightened from different angles of light for the photometric stereo algorithm. The stray light protection cones  block thSICSURFIS HSI’s modules, sensor, spectral separator and LEDs are independently controllable, which enables effective configuration via software. Imagers spectral range is 475–975 nm and the spectral resolution  8–18 nm. The average FWHM in selected spectral channels in this study was ∼10 nm. In this study, we utilised 33 selected spectral channels, but the imager is, depending on the calibration, capable of capturing hundreds or even thousands of spectral channels. The imager’s pixel resolution is approximately 24 μ to 6 mm .The SICSURFIS HSI with its LED module and stray light protection cones is a small-sized hand-held device, the overall weight of the imager is 880 g. The imager is easy to apply to patients’ skin and it reaches the challenging places. The patient does not have to move, so imaging is also possible in patients with reduced mobility. The imager is a spectral scanner, but it is almost as fast as a snapshot. Nor does it describe hundreds of wavelength ranges and thus add extra dimensions to the data. The number of wavelengths and the wavelengths of interest can be selected on a case-by-case basis, just as the LEDs can be individually controlled to match the current wavelength. This study used the wavelengths that had spectral absorption peaks in tissue chromophores such as melanin, haemoglobin, water, beta-carotene, collagen, and bilirubin .The size and adaptivity are advances compared to devices used in previous studies. Example images of previous and current HSI systems can be seen in these articles ,20,21,22The imager consists of a Piezo-actuated metallic mirror Fabry–Pérot Interferometer (FPI), an RGB sensor and an LED light source. The FPI controls the light’s transmission to the RGB sensor, and the role of the separate long and short pass filters, shown in Those FPI orders are matched with different sensitivities of the image sensor channels. There are three wavelength channels with different pixels in the RGB sensor’s Bayer pattern. If we carefully select the FPI air gap range, the RGB sensor will receive one to three transmission peaks. After recording the transmission peaks, the different spectral responses of the red, green and blue pixels can be seen throughout the selected spectral range ,23.n is the number of pixels. We are interested in reconstructing radiance C. Now, the radiance is:Before spectral calibration, we demosaic each Bayer pattern frame using bilinear interpolation, so that response in each pixel is now vector  so thatS=CRλ. FrThe LED illumination system of the SICSURFIS HSI is designed for the photometric stereo imaging setting. The LED light source module is a driver electronics PCB with 27 LEDs. The inner radius of the ring is 33 mm. The LED light source module has 27 pieces of intentionally selected 5 mm LEDs: white, 680, 720, 750, 780, 810, 850, 880 and 940 nm. Three series of these 9 LEDs are tilted by an angle of 30 degrees relative to the system optical axis. LEDs and FPI positions are individually controllable. The captured wavelengths, selected with FPI, can be combined with according LED illumination via software. The optics of the SICSURFIS HSI is designed with commercial S-mount and C-mount lenses, which provide collimated light beam through the Piezo-actuated FPI tunable filter. The selected optical design is described in Before the monochromator calibration, the transmission spectra characterization of the selected FPI module was carried out. The spectra were measured with the Ocean Optics HR4000 spectrometer at the center of the FPI. The monochromator calibration of the SICSURFIS HSI camera was performed using the setup shown in The combined sensitivities of the FPI and red, green and blue pixels were determined for over 200 FPI air gaps. In the calibration data analysis, the coefficients for the combined sensitivities of the FPI and red, green and blue pixels are retrieved in such a way that the linear combination of the R-, G- and B-pixel sensitivity signals contains none zero signal only at one spectral band.In the monochromator calibration, the signals of the calibrated optical power meter (Thorlabs PM16-120 USB Power meter) and the SICSURFIS HSI were recorded for the wavelength range 475–975 nm at 2 nm intervals and the Full-Width Half Maximum (FWHM) resolution of 5 nm.The results of the system calibration for SICSURFIS HSI are the raw measurement data of the monochromator calibration, the spectral response functions of R-, G- and B pixels for Piezo-actuated FPI (PFPI) setpoint voltages used in the calibration, and the system calibration matrices that can be used to calculate the spectral radiance or photons at the input aperture of the hyperspectral camera optics from the raw pixel signals. The exposure time and the camera gain for pixels must be known relative to the exposure time and pixel gains used in the monochromator calibration in order for absolute calibration to be valid. Examples of the measured combined sensitivities of the FPI and red, green and blue pixels and linear combinations of these sensitivities are presented in the next section.The LED light source module’s spectral radiances were measured using calibrated fiber, HR4000 spectrometer and white balance reflectance target, using the setup shown in The purpose of the monochromator calibration is to determine the combined sensitivities of the FPI and red, green and blue pixels see . The peaIn Photometric stereo is an imaging setting where the imaged object is illuminated from multiple angles, and corresponding images are captured with a stationary camera. When the angles of the lights are known compared to the imaged object, one can calculate the surface normals of the imaged object based on the intensity of gathered light in different imaging angles . From thThe normal maps for each wavelength are calculated by multiplying the inverse matrix of the light direction matrix by the three reflectance values for each pixel, corresponding to each light direction. The light directions are defined by the length from the imaged object to the center of the LED-module, N and albedo a matrices for each wavelength band are calculated asr mean its x-coordinate, y-coordinate, and the used light direction, respectively. The resulting matrix, which is of shape Now the normal x- and y-direction in every point  and denote them by p and q:From this, we can calculate the partial derivatives of the depth in p and q map:Now we calculate the Fourier transform Now the estimated surface map is:found in .The clinical pre-test section is divided into four sections. The background  and instThe clinical data gathering was performed in two phases during the spring and autumn of 2020. For the demonstrative pilot-test in this study, we examined two types of skin lesions, malignant basal cell carcinomas (BCC) and benign intradermal nevi (ID), with a clinically similar appearance.The HS images were captured in the first phase, and the lesions were clinically diagnosed and annotated by dermatologists at Helsinki University Hospital. In the second phase, we calculated the surface models of the skin surfaces using the methods of photometric stereo imaging. The aim was to classify the pixel spectra of BCCs, IDs, and healthy skin. We used a convolutional neural network and pixels selected from the albedo images and the lesions’ depth data.We captured images of 14 BCCs and 8 IDs on 21 volunteering patients with HS, digital and dermoscopic imaging and subsequently removed the lesions for dermatohistopathological analyses to confirm their diagnoses. All volunteering patients provided their written informed consent. The study protocol followed the Declaration of Helsinki and was approved by the Ethics Committee of Helsinki University Hospital.One of the typical challenges related to CNN classification and HS imaging is the limited availability of the labelled training data, which can lead the models to overfit . Thus weThe HS images were captured by dermatologists and nurses (users). The SICSURFIS HSI was combined with a specially designed hospital version of the CubeView software . CubeVieThe data capturing setup was designed for effortless workflow. All of the device and setup settings  were pre-assigned via software, and the imager was ready to work without any adjustments so that the user could concentrate on the patient.After the patient number and lesion number was given to the system, the user interface guided the user to capture the dark and white references. The dark acquisition was performed by manually placing the imager to a light-blocking holder, seen in The user interface provided a preview video to target the image while the HS imager was placed on the patient’s skin. Three different LED light and waveband combinations were captured with a single process and one click to the capture button. With one click, the system captured six HS images. After capturing the HS image, the software calculated reflectance frames automatically for each wavelength and visualized those frame by frame in an animation view. The quality of the HS images was ensured visually from the animation by the users. At the end of the effortless workflow, the user could save or dismiss captured HS images with one click.The user interface, the HS imager, and the chosen test setup (LEDs and correct wavebands) were pre-tested at the hospital with the users before the imaging for the clinical pre-test was started. The users were educated on the software and HS imager. The imager, dark and white reference targets and a computer with capturing software were mounted into a trolley so it could be stored in a secure place while unused a.One HS image consists of three sets of frames, captured with thirty-three selected wavebands. The visible light (VIS) had two LEDs on, and the visible and near-infrared (VNIR) bands had seven LED lights on during the capturing process. The selected wavelengths and corresponding led lights are shown in The HS images of the lesions had identification numbers. The dermatologist made the clinical diagnosis for each lesion before imaging, and the final diagnosis was confirmed with histopathological analysis of each lesion. The dermatologists hand-drew the ground truth images based on the histologically confirmed diagnoses.The images were saved as raw images and pre-processed twice. We call these stages raw image pre-processing and machine learning pre-processing. The raw image processing pipeline is described in Image quality reasons limited the number of the HS images from 14 BCCs to 10 and 8 IDs to 7. After the raw image pre-processing, each of the 17 selected HS image consisted of 33 albedo and 33 depth frames from the originally selected 33 wavebands.In the first phase, we selected the most significant depth frame, which was the frame representing the wavelength of 575 nm in this test setup. The wavelength channel was selected based on the measured data’s robustness while ensuring that the channel is well within the range of visible light. Using only one depth frame, we could reduce the unnecessary depth dimensions from the data. After reducing the dimensions, the data were normalised between 0 and 1, and the possible infinity and non-numeral (NaN) values on the areas outside the imagers field of view were set to 0.Images were vertically sliced from the middle of each lesion. The training pixels from the histologically confirmed lesions (250) and the healthy skin (100) were randomly selected from the left side of the HS image. The testing data were selected similarly from the right side of the image. The healthy skin pixels were selected by using a healthy skin mask, which was hand-drawn based on the ground truth images and ’RGB’ visualisations. The pixel selection is visualised in The training and testing subsets from the HS image were rolling window views of the selected pixels. The size of one window was 30 times 30 pixels with 34 channels, and the step was set to 5. The training subset with its 6160 windowed HS-sub-cubes were balanced with Imbalanced learn library’s random over-sampling method . After bThe classification maps shown in We validated the results by a leave-one-out approach to see if the pixel-wise slice half method causes biasing. The approach was conducted so that convolutional neural network (CNN) models were trained 17 times, omitting one lesion’s HS-sub-cubes  for the classification. The 2D convolutional layers extracted the features from the windowed The model was trained using Adam optimizer with default parameters and the categorical cross-entropy loss function.To assess the quality of the photometric stereo transformation of the reflectance images, we take a look at three surface models: Surface model of a ball a, a LegoAn example of albedo spectra with their deviation can be seen in The pixel-wise classification results of the slice half model in Confusion matrices  visualizThis subsection compares the results obtained with a pixel-wise slice half model and 17 leave-one-out models. The results were obtained using subsets of the windowed  using novel atomic layer decomposition techniques. One of these prototypes was presented by Trops et al. (2091). MEMS-based HS imagers can be significantly smaller and lighter with a faster frame rate. Besides the HS imager’s technical properties, the capturing time depends on the amount of the selected wavebands, so it could be further narrowed based on the suspected lesion type, making the image capturing faster.Some of the images had quality issues which led to the decision to leave them out of the study. One of the problems was the sharpness of the images. The device required manual focusing, and some of the surfaces were challenging. For example, the nasal tip could be too small to the field of view, and the camera could not be set to the skin without passing some light from the sides of the nose. The unwanted light could be removed by covering the side areas with a hand, but it might make the manual focusing and staying on a place while capturing more difficult. Another focusing issue might be that the device is in sharp focus when the target is 6–6.5 cm away from the lens. Small and complex skin areas  might come naturally closer than the required distance since the target might get inside the stray light protection cone. Similar quality issues have been raised in previous studies, e.g., in the study by Salmivuori et al.  uneven sThe size of the stray light protection cone affected the usability of the images. Some of the images were captured with the smallest stray light protection cone. Those images had too small areas of healthy skin around the lesions. With the surface models, the best results were obtained with the stray light protection cone diameters of 4.9 and 5.8 cm. Additionally, the LED setup caused some reflections on the skin surface, which was difficult for our model to classify. For future studies, we would recommend capturing multiple pictures from the same patient, both images of lesions and separate images of only healthy skin. That would enable using the smallest stray light protection cones in the study because the healthy pixels could be selected from a different image. The reflectivity of the skin is one issue to be assessed in future research. It could be solved with linear polarizers placed in front of the LEDs.According to the user feedback, the imaging system was easy to use and the technical issues found can be improved with system engineering. As seen in the results, the SICSURFIS HSI reached complex surfaces, which were mentioned to be excluded in previous studies . Eye coIn the calculated surface models, the accuracy deteriorates as a function of the distance from the image center. In the border areas of the images, the effect is visible from the visualisations in In our measurement setup, there are many moving pieces, and therefore the process of determining the individual light direction matrices was deemed inaccurate. The surface models are affected also by the penetration depth of the used light.In this study, we decided to use wavelength from visible light , benign ID and healthy skin lesion pixels. The nodular BCC’s confidence map  shows thThe second example collage  is a 12 Third collage  is an exThe numerical results of the pixel-wise slice half model’s test data  show relIn our study, the sensitivity is mainly impaired by inaccurate delineation of the lesions, which again seem to be caused mostly by light reflections and uneven colour of the healthy skin in the images. The sensitivity is also decreased because the sensitivity calculations compare the diagnosis separately for each pixel of a lesion, not by a voting method with one diagnosis per lesion only. Our test data pixels were selected randomly from the right side of the HS images lesion and healthy skin areas, and since the CNN is a three class pixel-classifier, the results of those pixels may contain two types of lesion or healthy skin pixels.These sensitivity and precision results are not directly comparable with previous studies , which As an important note, the used data contain an inherit selection bias which in some cases increases and in some cases decreases the accuracy. The slice half method also cause bias by increasing the the obtained accuracy. Therefore, the results are promising, but further research is needed. In the further studies, the results could be addressed also with a majority voting method, which would enable more discussion with previous research.Besides the promising results, this kind of clinical pre-study has limitations and concerns related to approaches and methods. The pixel-wise classification approach, using only one HS cube as a source for training, validation and testing is a common approach in HS image analysis and classification studies  , clinical practices, patient age distribution, users of the imagers and so on. If the inherent bias is not caused by a method, it is caused by a data set. The level of bias is important to estimate, and further research should address decreasing it. Deep learning algorithms, such as CNN, is detecting and distinguishing features automatically, giving them weights. Basically, it is a black box, which does not explain why the diagnosis is made . In thisTraditional image classification is different than pixel-classification mainly due to the spectral domain, and the fact that each pixel is classified one-by one. Previous image classification studies show, that in image classification, it is possible to train the model and classify sub-images with no sensible, visually interpret information, and depending on the sub-images size, to obtain even good accuracy levels. The reason of this is, that the model might be able to to detect and weight features also from the background; use other visual aspects the human eye does not see features . The siz200×200) .In our case, we used hyperspectral data and a pixel-wise classification approach. Each sub-cube contained albedo and depth channels, providing a spectrum for each pixel. Most of the training data were augmented, e.g., flipped, which differs the selected The sources for bias are various. For example if several data sets are collected in a controlled environment and same session, all of these HS images might contain characteristics that are caused by the image acquisition. So far, the machine learning model might classify them based on the session, not by the features seen in images . In our As shown by this discussion, the perceived worth of this study might be diminished by the bias. However, the reason for this clinical pre-study was to introduce and pre-test a new imaging system. Without the first models and results, it would have been impossible to evaluate the technical aspects; how the imager reaches the complex surfaces, can those images be classified by the CNN. It was important to test, how the imager’s special LED module, stray light protection and optical components affect on the image quality and to the results, and finally evaluate the clinical procedures. Based on this study, for example, the smallest protection cone has been rejected from the following data gathering steps and the stray light blocking in the challenging areas is improved with extra protection. One could say, that the accuracy of the leave-one-out approach is the real performance, but it is not the whole truth. Another opinion is, that due to the unseen features, the leave-one-out results reflect the selection bias on the collected data.The main point is not, what interpretation of the approaches and results is the right or how much and what kind of bias the collected data contains. The outcome is more in the potential, that the SICSURFIS HSI has, and in the results that can be obtained in the future via improvements. The results of the both approaches indicates that the proposed models would be practical with a larger amount of training data, which contains no unique lesions, and so far the selection bias could be minimised. The hypothesis for future studies is that the accuracy will increase when the collected data enables the models to generalise better. The accuracy difference between the leave-one-out method and the slice half method is expected to decrease as the data amount increases, and the level of selection bias should be taken into account in the further studies.In the following studies, we will increase the amount and variability of the data, improve the technical and user-related issues and capture more lesion types for developing a more effective CNN classifier to get one step closer to an optical biopsy. Current results are promising, but the need for further studies is obvious.This article aimed to introduce and demonstrate a Fabry–Perot interferometer-based hyperspectral imaging system for complex surfaces. This study was the first step in a three-phase pilot study demonstrating the possibilities of using a new system as a first prototype for future real-life applications. This section concludes our findings in technical aspects , methodoAs the described device is a prototype, there are still some issues to fix before achieving results that promote the devices use in clinical use. There is some variation in the focus between the spectral channels. Some of the frames might be slightly more out of focus than others. Bigger light protection cones provided better quality data than the smaller ones. There might not be enough healthy skin around the lesions with the small cones. The LED setup caused some reflections to the skin areas, which were difficult to handle for the convolutional neural network model. The depth maps were calculated for each wavelength, and one of the noted issues was the quality of the 3D models. The best quality was in the middle of the image. The quality deteriorated towards the edges of the frames. It is possible to improve the depth of the focus in the whole wavelength range by replacing the used commercial S- and C-mount lenses with a dedicated custom-designed optical system. The specular reflections can be reduced by using linear polarizers in front of the LEDs and the imaging optics.Based on the pre-test, the HS imager and machine learning system accurately differentiate malignant BCC from benign ID and healthy skin, achieving a weighted sensitivity of 0.79 and weighted precision of 0.81. The classification report reveals the results of the entire test data pixel-by-pixel classification. The results are not directly comparable with previous studies using voting methods for classifying pixel-wise the lesion types.We used a pixel-wise classification approach with CNN classifier. The approach is common with HS images, due to the lack of labelled HS data. The number of HS cubes used in this study was 17, which is generally 16 HS images more than in a typical HS image classification method development experiments; it is typical to use the pixels from one standard data set, and divide them to training, validation and test portions . In our For the comparison of validation to the slice half approach, only the results of the eight typical lesions should be taken into account. The average accuracy of those results was 0.72, whereas the similar average accuracy with the slice half approach was 0.89. These results are shown in This was a development starting point and a demonstration of a novel SICSURFIS HSI system, which seems to have potential, but the need for further studies is obvious. With its results and notes, the pre-test pointed out benefits and findings for many future improvement aspects.COVID-19 had a decreasing effect on the data gathering phase’s patient recruitment. As the system uses unique, specifically selected LEDs and wavelength bands, most of the previously gathered HSI data are inappropriate for training the machine learning algorithms used in the research. Therefore, data gathering with a similar device should continue in the future.As the dataset size increases, the leave-one-out cross-validation method should be adopted. The accuracy difference between the leave-one-out method and the slice half method is expected to decrease as the data amount increases. There is no risk for cross-contamination between training and testing data in the leave-one-out method.Before the clinical use, all of these issues should be inspected. Another technical step toward the future might be the MEMS FPI, which could reduce the size and weight of the handheld device. There is also a need for developing the system to be a real-time solution. It might mean the FPGA computation and a screen on top of the device for the doctors to see the results immediately.


The mechanism underlying the health care cost trajectories among asylum seekers is not well understood. In the canton of Vaud in Switzerland, a nurse-led health care and medical Network for Migrant Health (“Réseau santé et migration” RESAMI) has established a health care model focusing on the first year after arrival of asylum seekers, called the “community health phase”. This model aims to provide tailored care and facilitate integration into the Swiss health care system. The aim of this study is to explore different health care cost trajectories among asylum seekers during this phase and identify the associated factors.We detected different patterns of health care cost trajectories using time-series clustering of longitudinal data of asylum seekers in the canton of Vaud in Switzerland. These data included all adult asylum seekers and recipients of emergency aid who entered the canton between 2012 and 2015 and were followed until 2018. The different clusters of health care cost trajectories were then described using a multinomial logistic regression model.We identified a concave, an upward trending, and a downward trending cluster of health care cost trajectories with different characteristics being associated with each cluster. The likelihood of being in the concave cluster is positively associated with coming from the Eastern Mediterranean region or Africa rather than Europe and with a higher share of consultations with an interpreter. The likelihood of being in the upward trending cluster, which accrued the highest costs, is positively associated with 20–24-year-olds rather than older individuals, coming from Europe than any other region and having a mental disorder. In contrast to the other two clusters, the likelihood of being in the downward trending cluster is positively associated with having contacted the RESAMI network within the first month after arrival, which might indicate the potential of early intervention. It is also positively associated with older age and living in a group lodge.Asylum seekers are heterogeneous in terms of health care cost trajectories. Exploring these differences can help point to possible ways to improve the care and supporting services provided to asylum seekers. Our findings could indicate that early and patient-centered interventions might be well-suited to this aim.The online version contains supplementary material available at 10.1186/s12913-022-08346-y. Asylum seekers frequently live in situations of extreme vulnerability due to migration-related health risks, negative life events and low socio-economic status –3. They In the French-speaking canton of Vaud in Switzerland (covering 10% of the Swiss resident population ), a nursAs soon as asylum seekers and recipients of emergency aid arrive in the canton of Vaud, they are invited to visit one of the facilities of the Migrant Care Unit for health examinations, including immunization. During the first year, called the “community health phase”, they are also encouraged to visit the Migrant Care Unit as a first point of access to health care. With the “community health phase” newly arrived asylum seekers are meant to be integrated into the Swiss health care system. The Migrant Care Unit helps them manage issues related to language and cultural differences, and use efficiently the health care services. The Migrant Care Unit thus acts as a gatekeeper, referring individuals to physicians and specialists within the RESAMI network or the emergency department when needed, and offers follow-up consultations. It also provides them with information on disease prevention and health promotion. As long as they are included in the RESAMI network, individuals can, according to their needs, either continue their care at the Migrant Care Unit or use the “conventional” health care system.Previous studies have highlighted the need for primary health care among newly arrived asylum seekers , 18. TheAddressing asylum seekers’ complex needs can be challenging from a financial and organizational perspective for the host country, especially considering the heterogeneity among asylum seekers , 23, 24.In this study, we exploit rich longitudinal data collected by the RESAMI network to identify different trajectories of health care costs among asylum seekers, including recipients of emergency aid in the canton of Vaud. Emergency aid refers to the special residence status of dismissed asylum seekers and not to medical emergency. Moreover, we provide novel information on the characteristics that are associated with the differences between these health care cost trajectories and estimate the statistical relevance of the timing of the first contact with the RESAMI network. As such, we add to the existing literature by shedding new light on the potential mechanisms underlying the trajectories of health care among asylum seekers and recipients of emergency aid.Switzerland had in 2020 67,175 recognized refugees granted asylum and 55,638 asylum seekers , represeDespite the broad health care coverage in Switzerland, the health care system is very complex, making it even more difficult for asylum seekers to navigate. It is highly decentralized, including the processing of asylum seekers, which is organized differently in each of the twenty-six cantons . Upon arThe mandatory health insurance is covered for asylum seekers by the cantons throughout the entire process, even in the case of dismissal of asylum when the person is under the system of emergency aid . Each caThe study uses patient-level data collected by the different actors of the RESAMI network. This dataset includes all adult asylum seekers and recipients of emergency aid who entered the canton of Vaud between 2012 and 2015 and were followed until they were granted residence, they exited the country or until the end of the data extraction period on December 31, 2018. However, due to a data linkage problem, 11% of the original sample cannot be used (for more information on the data see ). This sThe dataset provides information on demographic and administrative characteristics , health care utilization , health care costsWe uncovered the different patterns of health care cost trajectories by applying a shape-based time-series clustering algorithm on the longitudinal health care cost data in the first twelve months after arrival. This is a data science technique that is applied to discover patterns and group homogeneous time-series together when there is no former knowledge about clusters . By asseTo remove random noise in health insurance data, we first smoothed the trajectories by bins that sum the health care costs of four consecutive months.We then also rescaled the trajectories by dividing the value in each bin by the mean of the three bins for each trajectory . We thereby replaced the health care costs with weights relative to the mean costs. This enabled a shape-based comparison between the trajectories that does not take the magnitude of the costs (or any other characteristic) into consideration.Next, we assigned the asylum seekers into the clusters of homogeneous shapes of health care cost trajectories by using the Euclidean distance for measuring dissimilarity between trajectories and the medoids as the centers of each cluster (i.e. the “centroids”) , 39. ThiWe assessed the differences between the clusters of health care cost trajectories using multinomial logistic regressions  with cluThe explanatory variables are time invariant characteristics, namely an indicator of whether the person had at least one consultation at the Migrant Care Unit within the first 30 days after arrival, an indicator of whether the asylum seeker is a recipient of emergency aid, sex, age groups, marital status, region of origin, language skills,To assess the association between the timing of the first contact with the Migrant Care Unit and the likelihood of being in each cluster, we first estimated a univariable model. In a second step, we estimated a multivariable model that fulfilled two purposes. First, the coefficients from the univariable estimation might be biased due to self-selection. With the multivariable estimation, we can control for observed characteristics that might impact this self-selection and assess the change in the coefficients of the timing variable. Second, the multivariable estimation allowed us to identify individual characteristics that are associated with each cluster. Therefore, while the univariable estimation was used to test a hypothesis, the multivariable was used for an exploratory analysis.The cluster analysis was conducted using R version 4.0.2. All other analyses were conducted using Stata version 16.1.After restricting the sample to individuals that had a length of stay of at least twenty months, 2,573 asylum seekers were identified, 14% of whom had been dismissed and therefore required emergency aid . In the second cluster, the health care costs are relatively stable between the first and the second four-month periods and then follow an upward trend (upward trending cluster). The health care costs in the third cluster are high during the first four months and then decrease sharply during the following four months followed by a slight downward trend (downward trending cluster).What differentiates the clusters are the timing and composition of health care costs. \* MERGEFORMAT Fig. In line with health care costs, the mean number of consultations is the highest in the upward trending cluster and the lowest in the downward trending cluster , asylum seekers from Europe compared to those of any other region and individuals with mental health conditions. By contrast, it is less likely to include recipients of emergency aid compared to other asylum seekers, females and individuals with pain or an inflammation. Additionally, a higher number of outpatient, other or Migrant Care Unit consultations increase the likelihood of being in this cluster, while a higher number of inpatient treatments decrease this likelihood.The downward trending cluster is more likely to include older individuals compared to 20–24-year-olds and individuals that live in a group lodge compared to individuals that have a private lease or live in individual accommodation. Both findings are noteworthy considering that age  and liviSensitivity analyses related to the health care utilization variables also showed similar results. The most noticeable differences were the insignificant coefficients of age groups and the fact that individuals with a mental disorder were statistically significantly less likely to be in the downward trending cluster. This association is probably gauged by the number of other consultations, which include psychotherapies, in the multivariable model shown in \* MERGEFORMAT Table In this study, we identified three main shapes of health care cost trajectories among asylum seekers or recipients of emergency aid during the first twelve months after their arrival. The upward trending cluster accounted for the highest share of the total health care costs (47%), followed by the concave cluster (33%) and by the downward trending cluster (21%). These shapes might reflect differences in demographic characteristics, health care needs and health care access among asylum seekers.We showed that individuals that had visited the Migrant Care Unit within the first month were more likely to have a downward trending health care cost trajectory. This trajectory is characterized by higher intensity of care in the first months mainly driven by inpatient and Migrant Care Unit costs, while Migrant Care Unit visits comprised the highest share of utilized services. This could reflect two cases. An early visit to the Migrant Care Unit could, on the one hand, lead to the detection and treatment of (pre-existing) health conditions, and subsequently to high costs in the first months that would otherwise have accrued later. On the other hand, it could lead to the (primary or secondary) prevention of health problems, thus preventing future costs , 21, 48.A noticeable factor of the upward trending cluster, which is described by high health care costs that persist throughout the twenty-month period, is that it is more likely to include people with a mental disorder. Along the same lines, Maier et al. 2010  have fouIn line with previous findings , 20, 22,Despite our novel analysis and findings, there are some limitations. First, the observed health care cost trajectories might not represent asylum seekers in cantons or countries without a patient-centered nurse-led health care network such as the one offered in the canton of Vaud. The possible impact of such a network on the health care cost trajectories could be evident in our study from the association between the timing of the first contact with the Migrant Care Unit and the likelihood of being in each cluster. However, the observed associations cannot be interpreted as causal due to the cross-sectional design used in the multinomial analysis. This constitutes the second limitation of our study. A possible way to identify causal relationships would be by comparing the canton of Vaud with a synthetic control group created on the basis of the other Swiss cantons that do not have such a nurse-led primary care system for asylum seekers. Third, despite the careful assessment of medication purchase, the identification of the disorders included subjective decisions that could induce measurement errors and bias. For example, some disorders can be treated without medication, and won’t be identified with the present methodology, while some medication can have different indications and can thus be used for different diseases. In this case, the most probable diagnostic category was selected, but we cannot know with certainty that the patient presented a particular disorder. Fourth, due to the data linkage problem, the sample might not fully represent all asylum seekers in the canton of Vaud. In addition, the share of individuals speaking French in our sample is higher than the share in the full sample of asylum seekers in the canton of Vaud described in Spycher et al. 2021 . This inFew studies have assessed the health care costs or their trajectories among asylum seekers and the potential of early care. These studies take different approaches to ours by providing valuable information on the aggregated trajectories of asylum seekers  or the eFrom a policy perspective, our study could thus be particularly interesting, as it points to possible ways policy makers could improve the care and supporting services provided to asylum seekers and in turn improve asylum seekers’ health care cost trajectories. For example, the shape of the downward trending cluster could indicate that early interventions might have positive implications on asylum seekers’ future health care costs. The positive association of the timing of the first contact with the Migrant Care Unit of the RESAMI network with the downward trending cluster, could also indicate that a nurse-led health care network might be well-suited to providing these interventions. Nurse-led primary care systems have been shown to provide appropriate treatment to asylum seekers , 57, 58.Moreover, the positive association of the upward trending cluster with mental health shows the association between mental health and high health care costs. At the same time, the fact that people with higher cost trajectories are more likely to not have visited the Migrant Care Unit within the first month could indicate the lost potential of the nurse-led health care network in providing appropriate health care to asylum seekers. This is especially relevant for mental health disorders considering its high prevalence in this population and the importance of the timing of offering culture-sensitive treatments . AlthougTherefore, this study indicates that a patient-centered nurse-led health care network could be well suited to offering early care adapted to asylum seekers’ specific health care needs. Nevertheless, further research that analyzes data on diagnosis, treatment, compliance with care, and health outcomes in regions with and without a nurse-led health care system should be conducted to gain a better understanding of the drivers of the shape of health care cost trajectories.We show that asylum seekers compose a heterogeneous group of people in terms of health care cost trajectories and provide first evidence on the characteristics that could distinguish these differences. Our findings could indicate that early and patient-centered interventions might be well-suited to providing appropriate health care to asylum seekers.Additional file 1:Fig. S1. Silhouette width by number of clusters.Additional file 2:Table S1. Descriptive statistics by cost category.Additional file 3:Fig. S2. Shape of a random draw of trajectories per cluster.