Abstract
Pneumonia remains a significant global health threat, especially among children, the elderly, and immunocompromised individuals. Chest X-ray (CXR) imaging is commonly used for diagnosis, but manual interpretation is prone to errors and variability. To address these challenges, we propose a novel attention-guided deep learning framework that combines spatial, temporal, and biologically inspired processing for robust and interpretable pneumonia detection. Our method integrates convolutional operations for spatial feature extraction, gated recurrent mechanisms to capture temporal dependencies, and spike-based neural processing to mimic biological efficiency and improve noise tolerance. The inclusion of an attention mechanism enhances the model's interpretability by identifying clinically relevant regions within the images. We evaluated the proposed method on a publicly available CXR dataset, achieving a high accuracy of 99.35%, along with strong precision, recall, and F1-score. Extensive experiments demonstrate the model's robustness to various types of image distortions, including Gaussian blur, salt-and-pepper noise, and speckle noise. These results confirm the effectiveness, reliability, and transparency of the proposed approach, making it a promising tool for clinical deployment, particularly in low-resource healthcare environments.