A systematic review on approach and analysis of bone fracture classification

Abstract

As a result of accidents or other injuries, bone fractures are becoming more common in our country. According to the India market survey study, fracture cases are becoming more common in Indian hospital records. The incidence rising by over 4.4 lakh in the last three decades and projected to reach over 6 lakh by 2020. The authors of this paper attempted to explain various forms of fracture detection techniques. This paper is folded into six halves. First, we will go through the introduction and data preparation step. Second, we discussed related work on fracture detection so far. Third, we look at different feature extraction methods that may be used to diagnose bone fractures. Fourth, we look at both traditional and deep learning-based methods for detecting bone fractures. Fifth, we looked at performance evolution approaches for determining the correctness of various algorithms. Sixth, we go through the many concerns and obstacles that researchers confront when working with fracture detection. The majority of authors are only concerned about whether the bone is broken or not, with very few concentrating on the classification of bone fractures. This paper aims to aid researchers in developing models that can automatically detect and classify fractures in human bones by providing a preliminary decision support system.