Prognosticate pulmonary pathosis for COVID negative and post-acute COVID patients using chest computed tomography images

Abstract

A significant number of studies have omitted data regarding fatalities (6 months–2 year) after the recovery from Corona Virus Diseases (COVID). Post-COVID, or long-term COVID, refers to the enduring consequences that individuals who have recovered from COVID-19 commonly suffer. People with chronic lung disorders are more likely to die as the infection progresses rapidly. Chest Computed Tomography (CT) images were used to identify the lung abnormalities and determine the patient's exact condition. The proposed method groups COVID-19-negative patients by chronic lung diseases, post-COVID lung disorders, and severity using an improved mask regional-convolutional neural network (R-CNN) to analyze chest CT scan images. Generate synthetic image with a cycle-consistency generative adversarial network (Cycle-GAN) to avoid overfitting. Enhanced Mask R-CNN using ResNet-101 by incorporating FPN model classifies COVID-negative patients' abnormal lung conditions as chronic or post-COVID disorder. This model achieves an accuracy of 95.71 %, F1 score of 94.21 %, a mean average precision (mAP) of 92.34 %, and a geometric mean (G-mean) of 94.89 %. Further post-COVID disorder can be classified into mild (structural abnormalities) or severe (fibrosis). This model had an accuracy of 90.38 % without using cycle-GAN and 94.35 % of accuracy by applying cycle GAN to generate synthetic images which balances the dataset for the severity classification of post-COVID disorder. It achieves the p-value as 0.0004 where p < 0.01 shows that the augmented dataset showed significantly higher performance. This method helps radiologists diagnose chronic lung disease or post-COVID disorder, which enables them to provide appropriate and effective treatments.