Detection of COVID-19 in X-Rays by Convolutional Neural Networks - Kadhim, Ola N.; Najjar, Fallah H.; Khudhair, Kifah T. (Iraqi Journal of Science, 2023)
Coronavirus is considered the first virus to sweep the world in the twenty-first century, it appeared by the end of 2019. It started in the Chinese city of Wuhan and began to spread in different regions around the world too quickly and uncontrollable due to the lack of medical examinations and their inefficiency. So, the process of detecting the disease needs an accurate and quickly detection techniques and tools. The X-Ray images are good and quick in diagnosing the disease, but an automatic and accurate diagnosis is needed. Therefore, this paper presents an automated methodology based on deep learning in diagnosing COVID-19. In this paper, the proposed system is using a convolutional neural network, which is considered one of the mostly prominent techniques used today for its reliability and ability to generate rapid results. The system was trained on a set of X-Ray images taken of the chest area of infected and uninfected people. The CNN structure gave accuracy, Precision, Recall and F-Measure 98%. This model is characterized by its ability to distinguish efficiently and adapt to different cases.