Evaluating the Performance of State-of-the-art Methods and Classifying Covid-19 Infected Tissues

In this study, the Traditional Convolution Neural Network (TCNN) and state-of-the-art approaches were applied to the datasets of Chest X-ray and CT scan imaging modalities and trained them concurrently. The TCNN's performance for detecting COVID-19 infected tissues was determined through a comparison examination using state-of-the-art approaches. The accuracy of the models has been improved by lowering the model's losses and overfitting. Finally, the training data size has been enhanced utilizing various picture augmentation methods such as flip-up-down, flip-down-left-right, and so on. VGG19 and InceptionV3 were tested in this work, and accuracy scores of 97 percent (X-ray images) and 96 percent (CT-scan images) were obtained. The model's loss functions, Precision, Recall, and F1-Score, were extracted and interpreted in the study. We examined the researchers' modified DL models and discovered that they were 65 percent accurate on X-ray data and 62 percent accurate on CT scan images. Experiments have demonstrated that when the number of sample images rises, the VGG19 and InceptionV3 perform well.