DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

Purpose: As of December 21, 2020, a total of 77,670,400 cases of coronavirus disease 2019 (COVID-19) have been confirmed worldwide, 53,825,243 cases have been cured and 1,693,253 cases have died. Among the diagnostic methods of COVID-19, chest X-ray images have the advantages of fast imaging, low cost and high accuracy of single plane lesions recognition. The current COVID-19 detection models have shortcomings such as weak robustness, unreliable generalization ability, and long training time. Methods: To solve the above problems, our team proposed two novel frameworks and five methods to diagnose COVID-19 based on chest X-ray images. (i) A novel framework – depthwise separable convolutional neural network (DCNN), and we tested Three methods, viz., using LeNet-5, VGG-16, and ResNet-18 as backbones. (ii) A novel framework – dilated and depthwise separable convolutional neural network (DDCNN), and we tested Two methods, viz., using VGG-16 and ResNet-18 as backbones. Results: Experiment results show that our models not only improve the detection accuracy, but also reduce the training time. Conclusions: Our methods are superior to state-of-the-art methods in both above aspects