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.…