COVID-19 Lung Image Classification Based on Logistic Regression and Support Vector Machine

Nazish, Ullah S. I.; Salam, A.; Ullah, W.; Imad, M.

COVID-19 is an infectious disease caused by SARS-Cov2 that has spread rapidly worldwide. According to the World Health Organization (WHO), the total cases of 4374783839 are reported from different countries. In this consequence, it is necessary to diagnose automatically COVID-19, which helps in prevention during spreading among people. In this study, we have used machine learning techniques to diagnose and classify the COVID-19 and normal patients from chest X-ray images using a machine learning technique. The proposed system involves pre-processing, feature extraction, and classification. In the pre-processing, the image is to enhance and improve the contrast. In the feature extraction, the Histogram of Oriented Gradients has been applied to extract the image's feature. Finally, in classification two different machine learning techniques (Support Vector Machine and Logistic Regression) have been used to classify COVID-19 and normal patients. The result analysis shows that the SVM achieved the highest accuracy of 96% and provide a better result than logistic regression (92% accuracy). © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.