Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification

Oladosu Oyebisi Oladimeji; Ayodeji Olusegun Ibitoye

doi:10.35377/saucis...1635644

EN TR

Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification

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.

Keywords

Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification

Öz

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.

Anahtar Kelimeler

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Details

Primary Language

English

Subjects

Computer Software , Software Engineering (Other)

Journal Section

Research Article

Authors

Oladosu Oyebisi Oladimeji ^*
0000-0001-8835-6156
Ireland

Ayodeji Olusegun Ibitoye
0000-0002-5631-8507
United Kingdom

Early Pub Date

September 24, 2025

Publication Date

September 30, 2025

Submission Date

February 10, 2025

Acceptance Date

July 12, 2025

Published in Issue

Year 2025 Volume: 8 Number: 3

DOI

https://doi.org/10.35377/saucis...1635644

IZ

https://izlik.org/JA28FD43NR

Cite

RIS / Bibtex

APA

Oladimeji, O. O., & Ibitoye, A. O. (2025). Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. Sakarya University Journal of Computer and Information Sciences, 8(3), 400-409. https://doi.org/10.35377/saucis...1635644

AMA

1.Oladimeji OO, Ibitoye AO. Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. SAUCIS. 2025;8(3):400-409. doi:10.35377/saucis.1635644

Chicago

Oladimeji, Oladosu Oyebisi, and Ayodeji Olusegun Ibitoye. 2025. “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”. Sakarya University Journal of Computer and Information Sciences 8 (3): 400-409. https://doi.org/10.35377/saucis. 1635644.

EndNote

Oladimeji OO, Ibitoye AO (September 1, 2025) Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. Sakarya University Journal of Computer and Information Sciences 8 3 400–409.

IEEE

[1]O. O. Oladimeji and A. O. Ibitoye, “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”, SAUCIS, vol. 8, no. 3, pp. 400–409, Sept. 2025, doi: 10.35377/saucis...1635644.

ISNAD

Oladimeji, Oladosu Oyebisi - Ibitoye, Ayodeji Olusegun. “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”. Sakarya University Journal of Computer and Information Sciences 8/3 (September 1, 2025): 400-409. https://doi.org/10.35377/saucis. 1635644.

JAMA

1.Oladimeji OO, Ibitoye AO. Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. SAUCIS. 2025;8:400–409.

MLA

Oladimeji, Oladosu Oyebisi, and Ayodeji Olusegun Ibitoye. “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, Sept. 2025, pp. 400-9, doi:10.35377/saucis. 1635644.

Vancouver

1.Oladosu Oyebisi Oladimeji, Ayodeji Olusegun Ibitoye. Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. SAUCIS. 2025 Sep. 1;8(3):400-9. doi:10.35377/saucis. 1635644