Enhanced Detection of Osteoporosis in Femur Bones with Improved VGG16

a major health concern, particularly for the aged, is osteoporosis, a common bone condition marked by decreased bone mineral density. Timely treatments and better patient outcomes can result from osteoporosis screening efforts that begin early. The use of deep learning algorithms to automate the diagnosis of osteoporosis using medical imaging data has shown encouraging results in recent years. For more precise diagnosis of osteoporosis in femur bones, this research suggests a new method based on an upgraded version of the VGG16 CNN architecture. In order to identify osteoporosis, the suggested model uses transfer learning that has already been trained on a large dataset of bone pictures. In order to make VGG16 work better for analysing femur bones, we add layers and optimize its design. A wide variety of femur bone scans, both healthy and with osteoporosis, annotated by seasoned radiologists, make up the dataset used for both training and assessment. When compared to baseline CNN models and more conventional approaches, our experimental findings show that our approach is far more sensitive and specific. Early intervention and improved disease management are made possible by the increased VGG16 model's resilience in identifying minor alterations in bone density that are symptomatic of early-stage osteoporosis. In addition, we evaluate the effectiveness and generalizability of our suggested strategy by rigorous comparative studies and validation.