TY  - JOUR
T1  - Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification
TT  - Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification
AU  - Oladimeji, Oladosu Oyebisi
AU  - Ibitoye, Ayodeji Olusegun
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.35377/saucis...1635644
JF  - Sakarya University Journal of Computer and Information Sciences
JO  - SAUCIS
PB  - Sakarya University
WT  - DergiPark
SN  - 2636-8129
SP  - 400
EP  - 409
VL  - 8
IS  - 3
LA  - en
AB  - 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.
KW  - Deep learning
KW  - Lung disease
KW  - Attention Fusion
KW  - Medical Image Analysis
N2  - 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.
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UR  - https://doi.org/10.35377/saucis...1635644
L1  - https://dergipark.org.tr/en/download/article-file/4590845
ER  -