Lightweight Edge-Aware Feature Extraction for Point-of-Care Health Monitoring

Osteoporosis classification from X-ray images remains challenging due to the high visual similarity between scans of healthy individuals and osteoporotic patients. In this paper, we propose a novel framework that extracts a discriminative gradient-based map from each X-ray image, capturing subtle structural differences that are not readily apparent to the human eye. The method uses analytic Gabor filters to decompose the image into multi-scale, multi-orientation components. At each pixel, we construct a filter response matrix, from which second-order texture features are derived via covariance analysis, followed by eigenvalue decomposition to capture dominant local patterns. The resulting Gabor Eigen Map serves as a compact, information-rich representation that is both interpretable and lightweight, making it well-suited for deployment on edge devices. These feature maps are further processed using a convolutional neural network (CNN) to extract high-level descriptors, followed by classification using standard machine learning algorithms. Experimental results demonstrate that the proposed framework outperforms existing methods in identifying osteoporotic cases, while offering strong potential for real-time, privacy-preserving inference at the point of care.