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CheXNet for the Evidence of Covid-19 Using 2.3K Positive Chest X-rays
4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021 ; 1576 CCIS:33-41, 2022.
Article in English | Scopus | ID: covidwho-1899021
ABSTRACT
CheXNet is not a surprise for Deep Learning (DL) community as it was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). In this paper, we study CheXNet to analyze CXRs to detect the evidence of Covid-19. On a dataset of size 4, 600 CXRs (2, 300 Covid-19 positive cases and 2, 300 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, we achieve the following performance scores accuracy of 0.98, AUC of 0.99, specificity of 0.98 and sensitivity of 0.99. On such a large dataset, our results can be compared with state-of-the-art results. © 2022, Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021 Year: 2022 Document Type: Article