Multi-Class Lung Disease Detection Using Attention-Based Deep Learning on Chest X-Ray Images
Keywords:
Attention mechanism, Chest X-ray, Deep learning, Grad-CAM, Hierarchical classification, Lung disease detection, Medical imaging, U-NetAbstract
The increasing prevalence of respiratory diseases such as COVID-19, pneumonia, and tuberculosis has significantly impacted global healthcare systems, necessitating the development of efficient, automated diagnostic solutions. Chest X-ray (CXR) imaging is one of the most widely used diagnostic tools due to its affordability, accessibility, and rapid acquisition. However, accurate multi-class classification of lung diseases remains a challenging problem because of overlapping radiographic features and variations in image quality. This paper proposes a comprehensive deep learning-based framework for multi-class lung disease detection using attention mechanisms and hierarchical classification. The proposed system integrates lung region segmentation using a U-Net architecture to isolate relevant anatomical structures and remove background noise. An attention-enhanced convolutional neural network (CNN) is employed to extract discriminative features, focusing on disease-specific regions within the lungs. Furthermore, a hierarchical classification strategy is adopted to first distinguish between normal and abnormal cases, followed by fine-grained classification of specific lung diseases. To improve model interpretability, Grad- CAM visualization is incorporated to highlight the regions influencing the model’s predictions. Experimental results demonstrate that the proposed system significantly improves classification accuracy, reduces misclassification among similar diseases, and enhances interpretability. The framework offers a reliable and efficient computer-aided diagnosis system that can support radiologists in clinical decision-making.
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