Detection by Clinicians Comparison of Intelligence Bone Fracture Detection System with SIFT algorithm for Identification of Bone Fracture

Abstract

The aim of the study was to detect by clinicians' comparison of the innovative Intelligence Bone Fracture detection system (IBFDS) with a sift algorithm for the identification of bone fracture. Material and Methods: The input images of X-Rays are collected from Kaggle.com. The sample size of 2 groups was 20. The data set contains bone fractures which are considered as an input image. MATLAB (2013) is used to compare an Intelligence bone fracture detection system with a deep neural network for accurate fracture detection. SPSS version 21 was used for the statistical analysis. A total of 20 samples were processed for the 2 groups to better accurately detect the bone fracture using a g power of 80%. Results: The comparison of IBFDS over the deep neural network was done independent sample t-test using SPSS software. The accuracy appeared for Innovative IBFDS was 91.67% and for the neural network was 83.56. Significance was observed for the comparison of parameter accuracy through SPSS Version 2.1 and the accuracy for SIFT was minimum (77.34 ± 6.40) followed by IBFDS (64.69±13.68). The Independent sample test revealed statistical significance and got P (0.003). Conclusion: The SIFT algorithm has better accuracy than IBFDS for bone fracture detection.