Deep Learning and Feature Extraction Techniques for Early Prediction of Osteoporosis from Medical Imaging

Osteoporosis, a medical condition resulting from the loss of bone mass and microarchitectural deterioration, remains one of the most challenging problems in healthcare, particularly due to the difficulty in diagnosing the disease at an early stage. Early detection is crucial for preventing severe complications and improving patient prognoses. This study explores the potential of deep learning approaches, including Convolutional Neural Networks (CNNs) and transformer models, to develop an early osteoporosis predictive model based on medical imaging. To achieve this, we present an integrated framework that enhances image preprocessing by effectively removing noise, improving contrast, and normalizing images before they are fed into subsequent stages. Furthermore, we examine various feature extraction methods, ranging from traditional handcrafted techniques to advanced deep learning approaches capable of capturing osteoporosis-relevant patterns with high accuracy. The proposed methodology is evaluated by comparing its prediction accuracy with existing approaches and assessing its generalizability across different imaging modalities, including X-ray, CT, and MRI. The results demonstrate that incorporating deep learning techniques, along with optimized image preprocessing and feature extraction, significantly improves prediction performance compared to conventional methods. This study contributes to the advancement of AI-driven diagnostic approaches in medical imaging, reinforcing the potential of deep learning in enhancing early osteoporosis detection and improving clinical decision-making for better patient outcomes.