An explainable AI-driven hybrid SE-transformer architecture for robust knee osteoporosis classification from X-ray data

Background Early and accurate classification of knee osteoporosis from radiographic images is crucial for timely diagnosis and treatment, yet existing deep learning models often lack both contextual understanding and interpretability. Objectives To develop an explainable hybrid deep learning framework that combines Squeeze-and-Excitation (SE) networks, Transformer architecture, BiLSTM, and BiGRU for improved knee osteoporosis classification. Methods The proposed model integrates SE blocks for channel-wise feature enhancement, a Transformer for capturing long-range spatial dependencies, and BiLSTM/BiGRU layers for sequential feature learning. Grad-CAM is employed to provide visual explanations of model predictions. Performance is evaluated using a stratified 70:20:10 training, validation, and testing split. Results The framework achieved an accuracy of 91.0%, precision of 91.0%, recall of 90.2%, and an F1-score of 90.4% on the test set. The reported results are based on a single-dataset evaluation and comparisons under identical experimental conditions. Conclusion The proposed SE-Transformer–BiLSTM–BiGRU framework delivers accurate and interpretable knee osteoporosis classification, demonstrating its potential for computer-aided diagnosis while warranting further validation on external datasets.