A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

Currently, the most widespread infectious illness in the world is the coronavirus (COVID-19). The original diagnosis of this illness presents the most obstacle in preventing subsequent infections and their transmission from one person to another. Therefore, it is crucial to employ both a clinical process and an automated diagnostic technology for the quick detection of COVID-19 to stop its spread. Chest X-ray (CXR) images from chest radiography could be used in artificial intelligence (AI) approaches to diagnose COVID-19 with excellent diagnostic accuracy. In this research, a new support vector machine kernel (SVM Kernel) and convolutional neural network (CNN) combination is suggested to classify COVID-19 using X-ray images. The fact that there are relatively few studies in the literature that provide novel solutions, particularly for regression issues, the goal of this study is to look into the creation of new SVM kernels. To categorize CXR pictures into the three categories of COVID-19, pneumonia, and normal utilizing pre-trained CNN models such as AlexNet, ResNet50, ResNet101, VGG-16, and VGG-19, this study proposes a revolutionary SVM Kernel. The results of the suggested approach show that the updated SVM Kernel may be used as a more effective forecasting tool. ResNet50 offers the greatest accuracy 96.2% and produces the best optimization results in a very short amount of time.