Development of a computer-aided tool for detection of COVID-19 pneumonia from CXR images using machine learning algorithm. Issue 1 (March 2022) - Absar, Nurul; Mamur, Baitul; Mahmud, Abir; Emran, Talha Bin; Khandaker, Mayeen Uddin; Faruque, M.R.I.; Osman, Hamid; Elzaki, Amin; Elkhader, Bahaaedin A.
Abstract: The novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide, and it has become a greater risk for human beings. To curb the community transmission of this virus, rapid detection and identification of the affected people via a quick diagnostic process are necessary. Media studies have shown that most COVID-19 victims endure lung disease. For rapid identification of the affected patient, chest CT scans and X-ray images have been reported to be suitable techniques. However, chest X-ray (CXR) shows more convenience than the CT imaging techniques because it has faster imaging times than CT and is also simple and cost-effective. Literature shows that transfer learning is one of the most successful techniques to analyze chest X-ray images and correctly identify various types of pneumonia. Since SVM has a remarkable aspect that tremendously provides good results using a small data set thus in this study we have used SVM machine learning algorithm to diagnose COVID-19 from chest X-ray images. The image processing tool called RGB and SqueezeNet models were used to get more images to diagnose the available data set. Our adopted model shows an accuracy of 98.8% to detect the COVID-19 affected patient from CXR images. It is expected that our proposed computer-aided detection tool (CAT) will play a key role in reducing the spread of infectious diseases in society through a faster patient screening process.