Systematic Mapping Study on the Use of Different Approaches for Detecting the Femoral Fracture Types using X-ray Images

Abstract

Usually the X-ray images can be used to detect fractures and help the physician to provide the appropriate diagnosis. To avoid further injury or causing more harm to the injured area, it is crucial to treat any broken or fractured bones as medical emergencies and seek the necessary treatment without delay. A thorough analysis was carried out to discover the previous studies in detecting the fractures using X-ray images by incorporating different techniques. Especially, as we all know artificial Intelligence and Machine Learning play a vital role in the medical domain in various aspects nowadays. During this mapping study, various fracture detection using image processing techniques were considered. Studies related to the analysis on X-ray images to detect fractures from 2010-2021 presented along with the findings discovered. The related studies extracted from six electronic archives namely Springer Link, IEEE Xplore, ACM Digital Library, Research Gate, and Science Direct. From the studies obtained there were no studies reported yet to classify the type of femoral neck fracture. There are four types of neck fractures that can occur in the femoral. It’s difficult for a doctor or radiographer with less experience to detect the exact type of femoral neck fracture in limited time. Anyhow to expand this study further, to detect the femoral fracture type in advance, previous studies related in detecting the other fractures using X-rays were analyzed. Totally around 200 studies were initially chosen, and 12 papers were shortlisted from the depth analysis. Numerous studies have been done on the identification of bone fractures. There have been attempts to use Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Local Binary Patterns (LBP) approaches frequently. This study would be a promising start for the future scholars to more precisely focus on detecting the type the femoral neck fracture in advance by adapting prevailing Machine and Deep Learning techniques.