Deep Learning-Assisted Efficient Staging of SARS-CoV-2 Lesions Using Lung CT Slices

Abstract

At present, COVID-19 is a severe infection leading to serious complications. The target site of the SARS-CoV-2 infection is the respiratory tract leading to pneumonia and lung lesions. At present, the severity of the infection is assessed using lung CT images. However, due to the high caseload, it is difficult for radiologists to analyze and stage a large number of CT images every day. Hence, an automated, computer-assisted technique for staging SARS-CoV-2 infection is required. In this work, a comparison of deep learning techniques for the classification and staging of different COVID-19 lung CT images is performed. Four deep transfer learning models, namely, ResNet101, ResNet50, ResNet18, and SqueezeNet, are considered. Initially, the lung CT images were preprocessed and given as inputs to the deep learning models. Further, the models were trained, and the classification of four different stages of the infection was performed using each of the models considered. Finally, the performance metrics of the models were compared to select the best model for staging the infection. Results demonstrate that the ResNet50 model exhibits a higher testing accuracy of 96.9% when compared to ResNet18 (91.9%), ResNet101 (91.7%), and SqueezeNet (88.9%). Also, the ResNet50 model provides a higher sensitivity (96.6%), specificity (98.9%), PPV (99.6%), NPV (98.9%), and F1-score (96.2%) when compared to the other models. This work appears to be of high clinical relevance since an efficient automated framework is required as a staging and prognostic tool to analyze lung CT images.