Reliable Covid-19 Detection using Chest X-Ray Images

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 re...

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Bibliographic Details
Main Authors: Degerli, Aysen, Ahishali, Mete, Kiranyaz, Serkan, Chowdhury, Muhammad E. H., Gabbouj, Moncef
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Computer network reliability
COVID-19
COVID-19 Detection
Deep Learning
Machine Learning
Machine learning algorithms
Pulmonary diseases
Reliability
SARS-CoV-2
Sensitivity
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506442