Khan, SamihaIslam, S M MahsanulNasib, Abdullah UmarHasnat, FahimHasan, Md. MazidulMim, SumaiyaBin Sayed, JawadMehedi, Md Humaion KabirIqbal, ShadabRasel, Annajiat Alim2026-08-152026-08-152022-01-01S. Khan et al., "COVID-19 Classification from X-Ray Images using 2D CNN," 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 2022, pp. 13-18, doi: 10.1109/R10-HTC54060.2022.9929517.9781665401562257276212-s2.0-85142101911https://hdl.handle.net/10361/29080The coronavirus (COVID-19) detection has been a crucial task for researchers, scientists, health experts all across the world and everyone is trying together to find a solution to it. The X-rays images of lungs have become one of the most prevalent and effective procedures used by researchers to monitor COVID-19. Unfortunately, inspecting each case involves multiple radiology experts and time, which is one of the critical tasks in such an outbreak. In this paper, a deep learning approach, 2D convolutional neural networks (CNN) has been used to classify healthy and COVID-19 chest X-ray images. 'Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays)' dataset has been used in this study. The major indicator of this study is the accuracy of the proposed model. The classification model, 2D CNN has achieved accuracy and f1-score of 0.96 and 0.95 respectively.13-18en-USfalse2D CNNClassificationCoronavirusDeep learningImagesX-RAYCOVID-19 (Disease).Machine learning.Conference Proceeding10.1109/R10-HTC54060.2022.9929517