AI-Powered Insights: VGG-19 for Scalable and Accurate Knee Osteoporosis Detection

Osteoporosis causes bones to become thinner and increases the odds of breaking them. Weight- bearing joints such as the knee have been seriously affected by this disease. To stop the condition from advancing and to help save costs, the disease is diagnosed early. Commonly, people use X-rays and MRIs to diagnose, yet those tests are costly and call for trained experts. A VGG-19 model was adopted in this paper to classify knee images as falling into one of two classes: healthy or osteoporotic. The accuracy of the VGG-19 model after it was trained on 5,000 knee images reached up to 97.5%. The model was very precise, resulting in few false positives and false negatives which is essential in clinical work. Since deep learning and transfer learning are used, the model offers an inexpensive and flexible approach for diagnosing cancer early, without depending heavily on costly imaging techniques. This approach might lead to better patient results by allowing quick action and fewer invasive therapies. These results suggest that using a CNN like VGG-19 in deep learning could transform how knee osteoporosis is identified by healthcare systems globally. To achieve better performance, the future work should focus on diversifying the data and bringing in multimodal sources.