| Title |
Automated Detection of COVID-19 in Chest Radiographs: Leveraging Machine Learning Approaches |
| Authors |
(Raheela Batool) ; (Ghulam Musa Raza) ; (Usman Khalid) ; (Byung-Seo Kim) |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.6.572 |
| Keywords |
COVID-19 pandemic; Machine learning models; Chest X-ray classification; Automated identification; Medical diagnosis |
| Abstract |
The World Health Organization (WHO) has designated the COVID-19 pandemic a global health emergency, prompting responses all over the world. The fatality rate is between 2% and 5%, and millions of people around the world have been infected. While the WHO recommends tests, resource-intensive testing has motivated the development of CNN technology for automated identification. Research employing machine learning models shows great accuracy in classifying X-ray and CT images for COVID-19 detection. These models include denseNet201, resnet50V2, inceptionv3, mobile net, and custom CNNs. The interpretation of chest X-rays has come a long way, yet there are still obstacles to overcome. In this paper, we present a way for using a machine learning model to categorize chest X-ray pictures into normal, COVID-19, viral pneumonia, and lung opacity, demonstrating the model's efficacy in assisting medical diagnosis, especially in time-sensitive situations like COVID-19. |