Improving early detection of Osteoporosis by bone densitometry functionalities using convolutional neural network – the systematic future for Para Athletes

Osteoporosis is a clinical condition where the bone mass density is decreased and leads to bone fracture and bone disability. In recent days the bone mass density(BMD) and calcium supplement is more deficient in small ages. To measure the level of deficient and to detect the chances of osteoporosis, bone disability the major research is being originated in clinical circumstances. The early detection of osteoporosis is done by using bone mineral density that is central DXA test which is commonly used for analyzing BMD test. The comprehensive approach convolution neural network(CNN) with DXA test is modelled for determining the osteoporosis with qualitative detection of bone mineral density by taking longer computation time. To overcome the above drawback the R-CNN algorithm is proposed which improves the early detection of osteoporosis by using bone densitometry functionalities.The system shows the performance improvement when comparing with the existing systematic approaches.