Classification Of BMD Using Artificial Neural Network

Abstract

Osteoporosis is a disease that affects the bones, which are a very important part of the human body. It has a tendency to lessen the volume of bones and, as a result, affect the micro architecture of bone tissues. For decades, a variety of imaging techniques have been used to evaluate and investigate the micro architecture of disturbed and damaged bones to be able to discover bone density inadequacies. Image enhancement, filtering, classification, segmentation, and other preprocessing techniques are used in image processing to diagnose the afflicted bone structure and obtain crucial information about the distorted micro architecture pattern. In this research, we have gathered rudimentary knowledge of tissues like osteoblast and osteoclast as well as a comparison evaluation of few osteoporosis detection approaches based on image processing. The goal of this study is to determine the prevalence of osteoporosis and changes in bone mass as people become older, as well as to compare the bone health of seemingly healthy males, puberty of women, and menopause women. We used a 260-person ethical database, with 130 men, 80 women (before menopausal), and 50 women (after menopausal). Bone mineral density (BMD) was measured at the femoral neck using dual energy X-ray absorptiometry.