Improving Medical X-ray Imaging Diagnosis with Attention Mechanisms and Robust Transfer Learning Techniques

Improving Medical X-ray Imaging Diagnosis with Attention Mechanisms and Robust Transfer Learning Techniques

X-ray imaging remains a cornerstone in medical diagnostics for conditions such as bone fractures, knee osteoarthritis, and lung diseases. However, variability in image quality and dataset diversity presents significant challenges for automated analysis using deep learning models. This study addresses these issues by proposing an EfficientNet B0 architecture enhanced with a Convolutional Block Attention Module (CBAM) to improve classification accuracy and interpretability across multiple X-ray datasets: FracAtlas, Knee, and Lung X-ray. A robust preprocessing pipeline comprising LAB color space conversion, morphological filtering, gamma correction, Non-Local Means denoising, resizing, and normalization was applied to optimize image quality, with each step’s effectiveness verified through established image quality metrics. Additionally, geometric augmentation techniques were performed to increase dataset variability and improve model generalization. Comparative experiments with transfer learning, transformer-based, and attention-based models identified the attention-based EfficientNet B0 as the best performer. An extensive ablation study on the Knee X-ray dataset refined hyperparameters to maximize performance. The optimized model achieved high test accuracies of 98.09%, 97.12%, and 99.51% on FracAtlas, Knee, and Lung datasets, respectively. Further analyses, including noise robustness testing, k-fold cross-validation, and Grad-CAM visualization, demonstrated the model’s resilience, consistency, and explainability. The findings highlight the critical role of attention mechanisms in enhancing feature representation and generalization across heterogeneous medical imaging tasks. This work lays a foundation for reliable, interpretable AI systems that can support clinical decision-making, with future efforts focusing on expanding dataset diversity and real-world clinical validation to accelerate adoption.