SCNN: A Explainable Swish-based CNN and Mobile App for COVID-19 Diagnosis

COVID-19 has triggered 6.42 million death tolls, and more than 586 million confirmed positive cases until 10/Aug/2022. CT-based diagnosis methods need special expert knowledge, and the labeling procedure is tedious. We first propose a 12-layer CNN-based backbone network. Then, we utilize the Swish activation function to replace traditional ReLU. The multiple-way data augmentation is utilized to enhance the training set. Our model is named Swish-based CNN (SCNN). A web app is developed based on the proposed SCNN model. The SCNN model performs better than the ReLU-based backbone network and LReLU-based backbone network, indicating the effectiveness of the Swish function. The SCNN model achieves a sensitivity of 94.50 & PLUSMN; 1.06, a specificity of 95.25 & PLUSMN; 0.59, and an accuracy of 94.88 & PLUSMN; 0.65. It performs better than ten state-of-the-art COVID-19 diagnosis methods. Our SCNN model is promising in diagnosing COVID-19. The developed web app can help the users upload their own images and give the prediction results.

Automated summary

COVID-19 has caused 6.42 million deaths and over 586 million confirmed positive cases as of August 10, 2022. A 12-layer CNN-based backbone network called SCNN, utilizing the Swish activation function, is proposed. The SCNN model outperforms other backbone networks and achieves high sensitivity, specificity, and accuracy in diagnosing COVID-19. A web app based on the SCNN model is developed for users to upload images and obtain prediction results.