Deep-Learning-Assisted Highly-Accurate COVID-19 Diagnosis on Lung Computed Tomography Images

-- COVID-19 is a severe and acute viral disease that can cause symptoms consistent with pneumonia in which inflammation is caused in the alveolous regions of the lungs leading to a build-up of fluid and breathing difficulties. Thus, the diagnosis of COVID using CT scans has been effective in assisting with RT -PCR diagnosis and severity classifications. In this paper, we proposed a new data quality control pipeline to refine the quality of CT images based on GAN and sliding windows. Also, we use class-sensitive cost functions including Label Distribution A ware Loss(LDAM Loss) and Class-balanced(CB) Loss to solve the long-tail problem existing in datasets. Our model reaches more than 0.983 MCC in the benchmark test dataset. I. INTRODUCTION Severe acute respiratory syndrome coronavirus 2 (SARS-CoV -2) infection still plays a major role in world policy changes and continues to effect billions every day. The severity of the infection rises with each day and early diagnosis can be crucial for disease control. Pandemic on such a massive scale which has impacted about 83 million people in US itself, has put primary methods of diagnosis via RT -PCR under stress. A reliable COVID-19 classification method is needed to relieve the pressure of manual clinical diagnostics.