Detection of COVID-19 Using X-ray Image Classification

COVID 19 disease rooted in China, spread across other parts of the world and became a devastating pandemic. The detection of COVID-19 has become a crucial task in the medical sector because of the soaring cases and the paucity of pharmaceutical supplies for detection. Considering the urgency, an immediate auxiliary automatic detection system is required for early diagnosis of the disease and helps the affected patients to be under immediate care. In this work, we aimed to propose an automatic detection system based on lung X-ray images, as radiography modalities is a promising way of faster diagnosis. In this work, we built a machine learning model considering X-ray images taken from publicly available data sets of 2000 images. The relevant features from the images were taken for building the model, prior that proper segmentation was applied to the X-ray images. The X-ray images are prone to noise and spatial aliasing which leads the boundary to be indistinguishable, so proper image segmentation is required Comprehensive validation has been performed on different segmentation techniques, among those, Sobel demonstrated an accurate result, which is not only effective in detecting edges but also good in removing noises within the image. Further, the preprocessed image is fed to a support vector machine (SVM) model, which accomplished the maximum classification accuracy of 99.17%, also SVM achieved precision, recall, and F1 score of 99.24%,98.13%,98.68% respectively in predicting the COVID-19 versus other pulmonary diseases. Taking the advantage. the model can be helpful to medical persons that can be used as an initial screening of individuals.