COVID-19 detection method based on SVRNet and SVDNet in lung x-rays.
Purpose: To detect and diagnose coronavirus disease 2019 (COVID-19) better and faster, separable VGG-ResNet (SVRNet) and separable VGG-DenseNet (SVDNet) models are proposed, and a detection system is designed, based on lung x-rays to diagnose whether patients are infected with COVID-19. Approach: Combining deep learning and transfer learning, 1560 lung x-ray images in the COVID-19 x-ray image database (COVID-19 Radiography Database) were used as the experimental data set, and the most representative image classification models, VGG16, ResNet50, InceptionV3, and Xception, were fine-tuned and trained. Then, two new models for lung x-ray detection, SVRNet and SVDNet, were proposed on this basis. Finally, 312 test set images (including 44 COVID-19 and 268 normal images) were used as input to evaluate the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet models. Results: In the classification experiment of lung x-rays that tested positive and negative for COVID-19, the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet are 99.13%, 99.14%, 99.12% and 99.37%, 99.43%, 99.31%, respectively. Compared with the VGG16 network, SVRNet and SVDNet increased by 3.07%, 2.84%, 3.31% and 3.31%, 3.13%, 3.50%, respectively. On the other hand, the parameters of SVRNet and SVDNet are  and  , respectively. These are 61.56% and 55.31% less than VGG16, respectively. Conclusions: The SVRNet and SVDNet models proposed greatly reduce the number of parameters, while improving the accuracy and increasing the operating speed, and can accurately and quickly detect lung x-rays containing COVID-19.
['Rao|Kedong|K|https://orcid.org/0000-0002-1435-7815', 'Xie|Kai|K|', 'Hu|Ziqi|Z|', 'Guo|Xiaolong|X|https://orcid.org/0000-0002-2350-9964', 'Wen|Chang|C|https://orcid.org/0000-0001-7339-3130', 'He|Jianbiao|J|https://orcid.org/0000-0003-3524-8877']
COVID-19 deep learning transfer learning x-ray images