result of Covid 19 Studies with Deep LEarning

For this purpose, in this study, we propose an artificial intelligence-based method has that will speed up the diagnosis of the pandemic by saving labour and expense. In the study, X-Ray images were processed with the most up-to-date deep image processing techniques, and an objective decision support system was created, independent of the doctors’ expertise. Our proposed system can classify x-ray images as Normal, Covid -19, and Viral Pneumonia using pre-trained deep learning networks (AlexNet, MobileNetV2, GoogleNet, ResNet8, and ResNet50) with transfer learning (TL) and K-fold validation (KFCV). The overall accuracies of the networks (AlexNet, MobileNetV2, GoogleNet, ResNet8, and ResNet50) were 98%, 97. 4%, 97. 4%, 97. 4%, 98. 59% respectively. Also, for the average performance metrics values (Sensitivity, Specificity, Precision, F1-score, and Negative predictive value(NPV)). The best results were recorded by ResNet 50+TL+KFCV (97. 9% Sensitivity, 98. 9% Specificity, 97. 9% Precision, 97. 9 % F1-Score, and 98. 9% NPV ). It is easy to diagnose in the advanced stages of the disease. As with most diseases, early diagnosis is important in COVID-19. With the proposed system based on deep learning, a very useful tool in combating the pandemic has been created by determining the disease at an early stage. The proposed system can also be used in areas with a shortage of health personnel such as rural and remote areas.