A Comprehensive Review of Deep Learning-Based COVID-19 Detection Mechanisms Using CT Images

Abstract

The diagnosis of COVID-19 has become a highly focused research area that captures researchers’ attention worldwide. Although the results of RT-PCR have been regarded as the golden standard for diagnosing COVID-19, CT-based diagnostic systems also have their unique advantages, attracting numerous researchers continuously into the area of developing deep learning-based diagnostic systems that utilize CT images. This paper is committed to presenting a comprehensive review, including current dynamics, generalized framework and useful resources. To capture the pattern of the developed methods, this paper introduces a generalized framework containing two stages: segmentation and classification. Furthermore, various valuable online resources have also been collected to provide more datasets, existing implementations of diagnostic systems, and commonly adopted evaluation metrics to researchers that are new to this area for their better adaptation and contribution to this meaningful, life-changing field.