Detection of bone fracture area using convolutional neural network
الموضوعات : journal of Artificial Intelligence in Electrical Engineering
mohammad fatehi
1
,
Mohammad Reza baghaei
2
,
kiumars sharafi
3
,
mohammad moradi
4
,
mahdi saneie
5
1 - 4. Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran.
2 - 1. Department of Biomedical Shahid Beheshti, Tehran Branch, Tehran, Iran
3 - 4. Department of Sports Management, Kerman Branch, Farhangian University, Kerman, Iran.
4 - Department of Electrical Engineering, chamran Branch, chamran University, Kerman, Iran
5 - Department of Sports Physiology, Research sciences Branch, Islamic Azad University, Tehran, Iran
الکلمات المفتاحية: bone fracture, convolutional neural network, aria detection, Pelvis fracture.,
ملخص المقالة :
Diagnosis of the fracture site is done using CT-Scan images and based on the doctor's visual diagnosis. This work is very time-consuming and depends on the doctor and his expertise. Systemic methods can help doctors and specialists and can detect the fracture area and the fracture surface. In fractures, only the location of the fracture is determined, but if we want to diagnose the area, high expertise and experience is needed, or in some cases, MRI images are needed. Convolutional neural networks are very powerful in diagnosing diseases and medical complications and can diagnose them correctly. The high accuracy and ability of convolutional neural networks has made this method popular among researchers, and its use is becoming more widespread every day. In this method, fracture location and fracture depth were determined using convolutional neural network. In this work, first the fracture site and then the fracture area are determined. In this study, the location of hip fracture was detected with complete accuracy and the fracture area was obtained with 99.68 accuracy and 99.82% sensitivity. The obtained results indicate that the proposed method is a suitable method for fracture detection.
