Segmentation of COVID-19 Chest CT Images Based on SwishUnet

Abstract

Coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has been spreading since 2019 until now. Chest CT-scan images have contributed significantly to the prognosis, diagnosis, and detection of complications in COVID-19. Automatic segmentation of COVID-19 infections involving ground-glass opacities and consolidation can assist radiologists in COVID-19 screening, which helps reduce time spent analyzing the infection. In this study, we proposed a novel deep learning network to segment lung damage caused by COVID-19 by utilizing EfficientNet and Resnet as the encoder and a modified U-Net with Swish activation, namely swishUnet, as the decoder. In particular, swishUnet allows the model to deal with smoothness, non-monotonicity, and one-sided boundedness at zero. Three experiments were conducted to evaluate the performance of the proposed architecture on the 100 CT scans and 9 volume CT scans from Italian the society of medical and interventional radiology. The results of the first experiment showed that the best sensitivity was 82.7% using the Resnet+swishUnet method with the Tversky loss function. In the second experiment, the architecture with basic Unet only got a sensitivity of 67.2. But with our proposed method, we can improve to 88.1% by using EfficientNet+SwishUnet. For the third experiment, the best performance sensitivity is Resnet+swishUnet with 79.8%. All models with SwishUnet have the same specificity where the value is 99.8%. From the experiments we conclude that our proposed method with SwishUnet encoder has better performance than the previous method.