CLN: A multi-task deep neural network for chest X-ray image localisation and classification

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

Abstract

Chest X-ray (CXR) imaging is a widely used and cost-effective medical imaging technique for detecting various pathologies. However, accurate interpretation of CXR images is a challenging and time-consuming task that requires expert radiologists. Although deep learning methods have demonstrated high performance in CXR image classification, concerns over interpretability limit their clinical adoption. Localising pathologies on chest X-rays could improve interpretability and trust in these systems. In this work, we propose the Chest X-ray Localisation Network (CLN), a multi-task deep neural network designed to localise and classify pathologies in CXR images. Our proposed architecture was trained and evaluated on a subset of the ChestX-ray14 CXR data set, which included bounding box annotations of eight different pathologies from expert radiologists, achieving a maximum classification mean AUC score of 0.918 and a maximum localisation mean IoU accuracy of 0.855 for the eight examined pathologies (atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, and pneumothorax). Our approach outperformed state-of-the-art methods, demonstrating its potential as a reliable solution for computer-aided CXR image diagnosis, offering notable advantages over existing methods, including superior classification and localisation accuracy, reduced performance decay with increased IoU thresholds, and an overall simpler architecture.

Citation

Okolo, G. I., Katsigiannis, S., & Ramzan, N. (online). CLN: A multi-task deep neural network for chest X-ray image localisation and classification. Expert Systems with Applications, Article 128162. https://doi.org/10.1016/j.eswa.2025.128162

• Journal Article Type: Article
• Acceptance Date: May 11, 2025
• Online Publication Date: May 17, 2025
• Deposit Date: May 19, 2025
• Publicly Available Date: May 20, 2025
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Article Number: 128162
• DOI: https://doi.org/10.1016/j.eswa.2025.128162
• Public URL: https://durham-repository.worktribe.com/output/3953593