Automatic COVID-19 Detection Systems using Deep Learning Techniques from Chest X-ray Images

Abstract

Deep learning has made significant advances in the previous several years, leading to remarkable advancements in a variety of applications, including images categorization. Deep learning has emerged as a crucial methodology in the domain of medical imaging that has significantly improved the categorization of medical pictures. Convolutional neural networks (CNNs) have shown very high effectiveness in the detection of several illnesses, including different dental conditions, Parkinson's disease, Alzheimer's disease, malaria, and coronary artery disease. Similar to earlier instances, CNN shows a lot of potential for diagnosing COVID-19 patients via the use of medical imaging methods like computed tomography and chest X-rays. The World Health Organization (WHO) has officially designated the coronavirus illness COVID-19 as a worldwide pandemic. Identification of persons who have tested positive for COVID-19 is crucial for stopping the spread of this contagious illness. In this study, models like ResNet50, Alex Net, Google Net, Mobile Net, and Modified ResNet50s are developed for using chest X-ray pictures to detect COVID-19 patients. A dataset including 3,000 chest X-ray (CXR) images is used, encompassing examples of both COVID-19 positivity and negativity. All models are trained and confirmed using COVID-19 chest X-ray images and normal chest X-ray images. The Modified ResNet50 model with Channel Shuffle has an F1-score of 100%, a Receiver Operating Characteristic (ROC) curve area of 100%, and precision and accuracy of 100% and 100%, respectively. This research also compares the effects of increasing dataset size and convolutional layer alterations on classification performance.