Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155791,
        author = {Ahila T and Dr A C Subhajini},
        title = {Covid-19 Detection Using Chest X-Ray},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {1788-1792},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155791},
        abstract = {COVID-19 continues to have a devastating impact on the lives of people all around the world. It is vital to screen the affected patients in a timely and cost-effective manner in order to combat this disease. Radiological examination is one of the most plausible steps in achieving this goal, with chest X-Ray being the most readily available and least priced option. We present a Deep Convolutional Neural Network-based approach to detect COVID-19 +ve patients using chest X-Ray pictures in this research. The implementation of a semi-quantitative CXR assessment has resulted from the addition of useful assistance to clinicians and the stratification of disease risk. Both severity scores and CXR results diagnosed early stage COVID-19 disease in this study. CXRs abnormalities were detected in 278 of 350 patients (78%) at certain points of the disease course. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. We have created a graphical user interface (GUI) application for public use. This application can be used by any medical personnel on any computer to detect COVID +ve patients using Chest X-Ray images in a matter of seconds.},
        keywords = {COVID-19, Coronavirus infections, Deep learning, Pneumonia, X-ray.},
        month = {},
        }