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• W3041160213 abstract "Abstract The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome (SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose COVIDPEN - a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers." @default.
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• W3041160213 date "2020-07-10" @default.
• W3041160213 modified "2023-09-23" @default.
• W3041160213 title "COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans" @default.
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• W3041160213 doi "https://doi.org/10.1101/2020.07.08.20149161" @default.
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