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• W4285404792 endingPage "103977" @default.
• W4285404792 startingPage "103977" @default.
• W4285404792 abstract "Today, 2019 Coronavirus (COVID-19) infections are a major health concern worldwide. Therefore, detecting COVID-19 in X-ray images is crucial for diagnosis, evaluation, and treatment. Furthermore, expressing diagnostic uncertainty in a report is a challenging duty but unavoidable task for radiologists. This study proposes a novel CNN (Convolutional Neural Network) model for automatic COVID-19 identification utilizing chest X-ray images. The proposed CNN model is designed to be a reliable diagnostic tool for two-class categorization (COVID and Normal). In addition to the proposed model, different architectures, including the pre-trained MobileNetv2 and ResNet50 models, are evaluated for this COVID-19 dataset (13,824 X-ray images) and our suggested model is compared to these existing COVID-19 detection algorithms in terms of accuracy. Experimental results show that our proposed model identifies patients with COVID-19 disease with 96.71 percent accuracy, 91.89 percent F1-score. Our proposed approach CNN's experimental results show that it outperforms the most advanced algorithms currently available. This model can assist clinicians in making informed judgments on how to diagnose COVID-19, as well as make test kits more accessible." @default.
• W4285404792 created "2022-07-14" @default.
• W4285404792 creator A5044938921 @default.
• W4285404792 date "2022-09-01" @default.
• W4285404792 modified "2023-09-30" @default.
• W4285404792 title "Deep learning-based approach for detecting COVID-19 in chest X-rays" @default.
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• W4285404792 doi "https://doi.org/10.1016/j.bspc.2022.103977" @default.
• W4285404792 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35855833" @default.
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