COVID-19 Cases Detection from Chest X-Ray Images Using CNN Based Deep Learning Model

COVID-19 has recently evolved into one of the most serious life-threatening infections and has kept still circulating globally. COVID-19 can be limited to a considerable extent if a patient can know his or her COVID-19 infection at a possible earlier time and the patient can be isolated from other individuals. Recently researchers have explored artificial intelligence-based technologies like machine learning, and deep learning approaches strategies to find out COVID-19 infection. Individuals can detect COVID-19 infection using their phones or computers, obviating the need for clinical specimens or visits to a diagnostic center. This can significantly reduce the risk of spreading COVID-19 farther from a probable COVID-19 patient. Motivated with this context, we propose a deep learning model using CNN to autonomously diagnose COVID-19 infection from chest X-ray images. The dataset used to train our model includes 10293 X-ray images, with 875 X-ray images from COVID-19 cases. The dataset contains three different classes of tuple: COVID-19, pneumonia, and normal cases. The experimental outcomes show that the proposed model achieved 97% specificity, 96.3% accuracy, 96% precision, 96% sensitivity, and 96% F1-score, respectively.