COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

• Published in: Multimedia tools and applications Vol. 81; no. 21; pp. 30615 - 30645
• Main Authors: Haghanifar, Arman, Majdabadi, Mahdiyar Molahasani, Choi, Younhee, Deivalakshmi, S., Ko, Seokbum
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2022; Springer Nature B.V
• Subjects: Abnormalities; Artificial neural networks; Computer Communication Networks; Computer Science; Coronaviruses; COVID-19; Data Structures and Information Theory; Datasets; Decision making; Deep learning; Multimedia Information Systems; Neural networks; Pneumonia; Special Purpose and Application-Based Systems; Viral diseases; X-rays; COVID-19; Imaging features; CheXNet; Convolutional neural networks; Chest X-ray
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12156-z

One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.