Reliable COVID-19 Detection Using Chest X-ray Images

TL;DR

This paper introduces ReCovNet, a robust deep learning model trained on the largest COVID-19 chest X-ray dataset to accurately distinguish COVID-19 from various thoracic diseases and healthy cases.

Contribution

The study presents ReCovNet, a novel COVID-19 detection network trained on QaTa-COV19, the largest CXR dataset, improving diagnostic accuracy and robustness.

Findings

ReCovNet achieved 98.57% sensitivity.

ReCovNet achieved 99.77% specificity.

The dataset includes 124,616 images with 4,603 COVID-19 samples.

Abstract

Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.