Classification of Chest X-Ray Images using CNN for Detection of COVID-19 and Pneumonia


  • Ahmed Aburas Elmergib University


Deep learning, Convolution Neural Network, COVID-19, Pneumonia


- In the past few years, specifically after late 2019, a dangerous virus spread in the Republic of China called (SARS-CoV2), which caused the emerging of the corona disease (COVID-19). Before the discovery of appropriate vaccines, the basic steps to combat this disease, as recommended by the World Health Organization, were early detection and diagnosis, then complete isolation of patients and their contacts, in order to limit its rapid spread. X-Ray images of the chest are considered one of the most important diagnostic methods for this disease, but these images need skilled and efficient doctors to diagnose them efficiently, especially in the presence of great similarity between viral or bacterial pneumonia with the infection caused by the COVID-19. In this paper, a model is built using artificial intelligence and deep learning to classify chest X-ray images to facilitate and assist medical personnel in fast and correct diagnosis of the disease. This model, is designed using convolutional neural networks, classifies these images into three classes, which are (Normal - Pneumonia - COVID-19). The proposed model is trained using 6432 images, it is distinguished by its simple design, in addition to performance and high efficiency in classification compared to some other models, as its accuracy reaches 95%.



How to Cite

Aburas أ. . (2023). Classification of Chest X-Ray Images using CNN for Detection of COVID-19 and Pneumonia. Elmergib Journal Of Electrical and Electronic Engineering ISSN: 2959-0450, 2(1), 1–14. Retrieved from