HyRA-CXR: a hybrid residual-attention deep network for chest X-ray classification

Chest X-ray (CXR) interpretation is essential for diagnosing pulmonary diseases, yet manual reading remains slow and prone to human error, especially in high-volume or resource-limited settings. To address delayed diagnoses and improve clinical efficiency, this study introduces (HyRA-CXR), a hybrid residual-attention convolutional neural network for automated CXR classification. The proposed model integrates residual blocks to enhance gradient stability and dual attention mechanisms to focus on significant lung regions. Experiments are conducted on the publicly available Lung X-Ray Image dataset. Hyperparameters are optimized using KerasTuner as well as model evaluation is carried out using five-fold stratified cross-validation. HyRA-CXR achieved an average accuracy of 90.39%, outperforming DenseNet121 (89.38%) and Xception (89.12%) models. Also, the experimental results confirmed that both residual and attention modules contribute, as removing either reduced accuracy below 90%. Overall, the proposed model achieves competitive accuracy with maintaining a compact architecture (0.52M parameters), indicating its suitability for deployment in resource-constrained settings. Our code is publicly available at: https://github.com/Ahmeed-Suliman-Farhan/HyRA-CXR-A-Hybrid-Residual-Attention-Deep-Network-for-Chest-X-Ray-Classification.