IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification

Okolo, Gabriel Iluebe; Katsigiannis, Stamos; Ramzan, Naeem

Abstract

Background and Objective: Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert radiologists to view and interpret chest X-ray images can be a bottleneck, especially in remote and deprived areas. Recent advances in machine learning have made possible the automated diagnosis of chest X-ray scans. In this work, we examine the use of a novel Transformer-based deep learning model for the task of chest X-ray image classification. Methods: We first examine the performance of the Vision Transformer (ViT) state-of-the-art image classification machine learning model for the task of chest X-ray image classification, and then propose and evaluate the Input Enhanced Vision Transformer (IEViT), a novel enhanced Vision Transformer model that can achieve improved performance on chest X-ray images associated with various pathologies. Results: Experiments on four chest X-ray image data sets containing various pathologies (tuberculosis, pneumonia, COVID-19) demonstrated that the proposed IEViT model outperformed ViT for all the data sets and variants examined, achieving an F1-score between 96.39% and 100%, and an improvement over ViT of up to +5.82% in terms of F1-score across the four examined data sets. IEViT’s maximum sensitivity (recall) ranged between 93.50% and 100% across the four data sets, with an improvement over ViT of up to +3%, whereas IEViT’s maximum precision ranged between 97.96% and 100% across the four data sets, with an improvement over ViT of up to +6.41%. Conclusions: Results showed that the proposed IEViT model outperformed all ViT’s variants for all the examined chest X-ray image data sets, demonstrating its superiority and generalisation ability. Given the relatively low cost and the widespread accessibility of chest X-ray imaging, the use of the proposed IEViT model can potentially offer a powerful, but relatively cheap and accessible method for assisting diagnosis using chest X-ray images.

Citation

Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2022). IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification. Computer Methods and Programs in Biomedicine, 226, Article 107141. https://doi.org/10.1016/j.cmpb.2022.107141

• Journal Article Type: Article
• Acceptance Date: Sep 14, 2022
• Online Publication Date: Sep 23, 2022
• Publication Date: 2022-11
• Deposit Date: Sep 15, 2022
• Publicly Available Date: Nov 10, 2022
• Journal: Computer Methods and Programs in Biomedicine
• Print ISSN: 0169-2607
• Electronic ISSN: 1872-7565
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 226
• Article Number: 107141
• DOI: https://doi.org/10.1016/j.cmpb.2022.107141
• Public URL: https://durham-repository.worktribe.com/output/1191878