Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models

• Main Authors: Echtioui, A. (Author), Ghorbel, M. (Author), Hamida, A.B (Author), Khemakhem, R. (Author), Mhiri, C. (Author), Sagga, D. (Author), Zouch, W. (Author)
• Format: Article
• Language: English
• Published: Springer 2022
• Subjects: Chest X-ray; Chest X-ray image; Computerized tomography; Convolutional neural network; Convolutional neural networks; Coronavirus; Coronaviruses; COVID-19; CT; Deep learning; Health crisis; Learning models; Pathogenic virus; SARS; X ray radiography
• Online Access: View Fulltext in Publisher

 Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread. Artificial intelligence is one of the approaches commonly used by researchers to discover the problems it causes and provide solutions. These estimates would help enable health systems to take the necessary steps to diagnose and track cases of COVID. In this review, we intend to offer a novel method of automatic detection of COVID-19 using tomographic images (CT) and radiographic images (Chest X-ray). In order to improve the performance of the detection system for this outbreak, we used two deep learning models: the VGG and ResNet. The results of the experiments show that our proposed models achieved the best accuracy of 99.35 and 96.77% respectively for VGG19 and ResNet50 with all the chest X-ray images. © 2022, The Author(s) under exclusive licence to Biomedical Engineering Society.