NON-INVASIVE PREDICTION OF BONE DISORDER USING DEEP LEARNING

Bone diseases are serious health issues that can weaken or destroy bones, resulting in persistent discomfort, decreased mobility, and an increased risk of fractures. These illnesses include osteoarthritis, genetic abnormalities, and trauma-induced damage. Traditional diagnostic techniques such as CT scans and X-rays, despite their extensive use, pose risks to patients due to their radiation exposure and reliance on radiologists for interpretation. This study uses cutting-edge sensors combined with a microcontroller to offer a non-invasive diagnostic system for the early diagnosis of bone problems. These sensors detect important biomechanical factors, such as knee joint angles, pressure, and flexibility, to detect possible problems such as dislocation of the bones. A deep learning system that was particularly trained on an extensive dataset obtained from Kaggle is used to process the gathered data, and it achieves a high prediction accuracy of 90%. The results are displayed in a web application that offers real-time monitoring and alerts, enabling healthcare providers to detect and address bone disorders early. This innovative system provides a patient-friendly, cost-effective alternative to traditional imaging techniques, promoting early diagnosis and timely intervention, ultimately improving patient outcomes and enhancing bone health management.