From Knee X-rays to Insights: A Hybrid Deep Learning Approach for Osteoporosis Detection

Abstract

Osteoporosis is a bone-weakening disorder that allows fractures to go undetected until significant damage has been done. Conventional bone density scans are costly and not universally accessible, leading to delays in diagnosis. In order to evaluate knee X-rays and detect early indications of osteoporosis, this study presents a hybrid deep learning model that blends CNN (Convolutional Neural Network) with Bidirectional Long Short-Term Memory (BiLSTM) networks. Using CNN to extract spatial features and BiLSTM to identify sequential relationships in patterns produced from images, this method has demonstrated high precision and cost-effectiveness, making it a valuable tool in the healthcare environment. This approach has the potential to enhance osteoporosis care through earlier detection, reduced fracture risks, and improved patient outcomes.

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