Oladimeji, Oladosu Oyebisi and Ibitoye, Ayodeji Olusegun ORCID: https://orcid.org/0000-0002-5631-8507 (2025) Multi-scale adaptive attention framework for improved lung disease classification. Sakarya University Journal of Computer and Information Sciences (SAUCIS), 8 (3). pp. 400-409. ISSN 2636-8129 (Online) (doi:10.35377/saucis...1635644)

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Abstract

Lung diseases are a leading cause of morbidity and mortality, underscoring the need for accurate diagnostic tools. Chest X-ray imaging is commonly used for diagnosis, but existing deep learning methods often focus on binary classification and struggle with the complexity of lung diseases. To address these challenges, we developed the Multi-Scale Adaptive Attention Fusion Network (MSAAF-Net), a novel framework designed for enhanced lung disease classification. MSAAF-Net integrates multi-scale feature extraction with self-attention, spatial attention, and channel attention mechanisms, dynamically weighted through a class-aware module. This approach enables the model to capture both fine-grained and large-scale pathological features, improving classification across multiple disease classes. Evaluated on a publicly available chest X-ray dataset using five-fold cross-validation, MSAAF-Net achieved a classification accuracy of 93.53%, an F1-score of 93.76%, and
an AUC of 98.33%, surpassing state-of-the-art models. These results demonstrate MSAAF-Net’s ability to effectively manage the complexity of multi-class lung disease classification. The framework enhances automated diagnostic accuracy, supporting better clinical decision-making and advancing AI’s role in lung healthcare.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, lung disease, attention fusion, Medical Image Analysis
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	Publisher
Last Modified:	22 Jan 2026 10:11
URI:	https://gala.gre.ac.uk/id/eprint/52313

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