Multi-dimensional Dataset based Osteoporosis Detection Technique using Sonographic and Thermal Wave Images
Abstract
Medical data processing has reached a new milestone of processing and re-defining the approaches of validating and diagnosing using computer vision and computer aided devices. In this article, a novel approach of detection and classification of osteoporosis (Bone Fracture) is proposed and discussed. The technique is designed on multi-dimensional dataset processing platform such as thermal images screening and sonography datasets. The technique is aimed to process the multi-dimensional datasets modeling by a structured attributes extraction and alignment, followed by the process of classification and clustering. The classification is based on DWT validation approach and thus clustered threshold vector for decision making, the technique is aimed to achieve 93.76%of accuracy and 0.921 precessions over dual data types.