Breast Infrared Thermography Segmentation Based on Adaptive Tuning of a Fully Convolutional Network.
BACKGROUND
Accurate segmentation of Breast Infrared Thermography is an important step for early detection of breast pathological changes. Automatic segmentation of Breast Infrared Thermography is a very challenging task, as it is difficult to find an accurate breast contour and extract regions of interest from it. Although several semi-automatic methods have been proposed for segmentation, their performance often depends on hand-crafted image features, as well as preprocessing operations.


OBJECTIVES
In this work, an approach to automatic semantic segmentation of the Breast Infrared Thermography is proposed based on end-to-end fully convolutional neural networks and without any pre or post-processing.


METHODS
The lack of labeled Breast Infrared Thermography data limits the complete utilization of fully convolutional neural networks. The proposed model overcomes this challenge by applying data augmentation and two-tier transfer learning from bigger datasets combined with adaptive multi-tier fine-tuning before training the fully convolutional neural networks model.


RESULTS
Experimental results show that the proposed approach achieves better segmentation results: 97.986% accuracy; 98.36% sensitivity and 97.61% specificity compared to hand-crafted segmentation methods.


CONCLUSION
This work provided an end-to-end automatic semantic segmentation of Breast Infrared Thermography combined with fully convolutional networks, adaptive multi-tier fine-tuning and transfer learning. Also, this work was able to deal with challenges in applying convolutional neural networks on such data and achieving the state-of-the-art accuracy.
['Tayel|Mazhar Basyouni|MB|', 'Elbagoury|Azza Mahmoud|AM|']
D000465:Algorithms D001940:Breast D001943:Breast Neoplasms D005260:Female D006801:Humans D007090:Image Interpretation, Computer-Assisted D016571:Neural Networks, Computer D013817:Thermography
breast infrared thermography fully convolutional networks semantic segmentation fine-tuning AlexNet transfer
learning.