Automatic Classification of COVID-19 using CT-Scan Images

Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used the medical field early diagnosis prediction. The major aim of this study is to evaluate machine deep using computed tomography scan (CT-scan) images automated detection coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred fifty-seven (757) CT-scan from a public platform. applied four traditional classification methods predict COVID-19 learning. These SVM, AdaBoost, NASNetMobile, InceptionV3. Comparative analyses presented among models by considering metric performance factors find best model. results show that InceptionV3 model achieves better terms accuracy, precision, recall, Cohen’s kappa, F1- score, root mean squared error (RMSE), receiver operating characteristic- area under curve (ROC-AUC), comparison with other Covid-19 classifiers. Accordingly, approach recommended automatic assessments. This research can present second point view experts it save time researchers as standard detecting evaluated.