Abstract: COVID-19 has recently manifested as one of the most serious life-threatening infections and is still circulating globally. COVID-19 can be contained to a considerable extent if a patient can know their COVID-19 infection at a possible earlier time, and they can be isolated from other individuals. Recently, researchers have explored AI (Artificial Intelligence) based technologies like deep learning and machine learning strategies to identify COVID-19 infection. Individuals can detect COVID-19 disease using their phones or computers, dispensing with the need for clinical specimens or visits to a diagnostic center. This can significantly reduce the risk of spreading COVID-19 farther from a probably infected patient. Motivated by the above, we propose a deep-learning model using CNN (Convolutional Neural Networks) to autonomously diagnose COVID-19 disease from CXR (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 the tuple: COVID-19, pneumonia, and normal cases. The empirical outcomes show that the proposed model achieved 97%specificity, 96.3% accuracy, 96% precision, 96% sensitivity, and 96% F1-score, respectively, which are better than the available works, despite using a CNN with fewer layers than those.

Md Amirul Islam, Giovanni Stea, Sultan Mahmud and Kh. Mustafizur Rahman, “COVID-19 Cases Detection from Chest X-Ray Images using CNN based Deep Learning Model” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01305108

@article{Islam2022,
title = {COVID-19 Cases Detection from Chest X-Ray Images using CNN based Deep Learning Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01305108},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01305108},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Md Amirul Islam and Giovanni Stea and Sultan Mahmud and Kh. Mustafizur Rahman}
}