Author(s)

Md. Harun Or Rashid, Muzakkir Hossain Minhaz, Ananya Sarker, Must. Asma Yasmin, Md. Golam An Nihal

Department of Computer Science and Engineering, Bangladesh Army University of Engineering & Technology (BAUET), Qadirabad Cantonment, Natore, Bangladesh.

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

COVID-19, Chest X-Ray Images, CNN, Virus, Accuracy

Rashid, M. , Minhaz, M. , Sarker, A. , Yasmin, M. and Nihal, M. (2023) COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach. Journal of Computer and Communications, 11, 29-41. doi: 10.4236/jcc.2023.115003.