Detection of Covid-19 from chest X-ray scans using machine learning

Machine Learning (ML) can be used to track the disease and predict the growth of the epidemic. Several detection models for COVID-19 are developed. Due to the uncertainty and lack of essential data, many existing models have shown low accuracy in detection. In several technology domains, ML models have been used to define and prioritize adverse threat variables. This study proposes an improved model to analyses and detect the amount of COVID-19-affected patients. In this study, we propose a classification model that detect the infected condition through the chest X-ray images. A dataset containing chest x-ray images of normal people, people with pneumonia such as SARS and pneumococcus and other patients with COVID-19 were collected. Histogram of oriented gradients (HOG) is used for image features extraction. The images are then classified using Support Vector Machines (SVM), random forests and K-nearest neighbors (KNN). These results may contribute well in detecting COVID-19 disease.