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• W4294775907 abstract "The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment.The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images.The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results.The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques.The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection." @default.
• W4294775907 created "2022-09-06" @default.
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• W4294775907 date "2022-11-12" @default.
• W4294775907 modified "2023-10-02" @default.
• W4294775907 title "Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach" @default.
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• W4294775907 doi "https://doi.org/10.3233/thc-220114" @default.
• W4294775907 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36093719" @default.
• W4294775907 hasPublicationYear "2022" @default.
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