RELIABLE COVID-19 DETECTION USING CHEST X-RAY IMAGES
Author | Degerli, Aysen |
Author | Ahishali, Mete |
Author | Kiranyaz, Serkan |
Author | Chowdhury, Muhammad E. H. |
Author | Gabbouj, Moncef |
Available date | 2023-04-17T06:57:46Z |
Publication Date | 2021 |
Publication Name | Proceedings - International Conference on Image Processing, ICIP |
Resource | Scopus |
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. 2021 IEEE |
Language | en |
Publisher | IEEE Computer Society |
Subject | COVID-19 Detection Deep learning Machine learning SARS-CoV-2 |
Type | Conference Paper |
Pagination | 185-189 |
Volume Number | 2021-September |
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COVID-19 Research [811 items ]
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Electrical Engineering [2616 items ]