CNN-Based Osteoporosis Diagnosis: A Novel Approach to Bone Fragility Analysis

Osteoporosis, a bone condition that raises the possibility of fractures, is distinguished by its diminished bone density and structural deterioration. Conventional diagnostic methods, such as DEXA scans can be time-consuming and expensive despite their reliability. In this work, we developed a CNN-based approach that employs deep learning to analyze medical images to diagnose osteoporosis and facilitate the early detection of bone fragility. We conducted experiments on osteoporosis datasets that were accessible to the general population to evaluate the model's efficiency. Our proposed model outperforms conventional techniques in terms of F1 score, precision, recall, and accuracy as indicated by the findings. The objective of this work is to increase osteoporosis diagnosis accuracy and consistency for early disease intervention.