Ground Truth Annotated Femoral X-Ray Image Dataset and Object Detection Based Method for Fracture Types Classification

Abstract

Precise classification of femoral fractures contributes to accurate surgical strategies and better prognosis after surgery. An effective and accurate system for diagnosing femoral fractures and classifying its types will play a vital role in clinical work. This work aims to achieve the automatic detection and classification of femoral fractures in X-ray images. We build a benchmark which includes 2333 X-ray images with 9 different fracture types, and each of them is manually labeled with the ground truth boxes indicating the femoral shaft fractures and its corresponding categories according to the Association for the Study of Internal Fixation (AO). An anchor-based Faster RCNN detection model, with the backbone of ResNet-50 being constructed in a multi-resolution feature pyramid networks (FPN), is used for locating fractures regions and classifying its types. The total image level accuracy reaches 71.5%, which is higher than some of the orthopedic surgeons can get, especially young orthopedic surgeons. Therefore, it is practicable to take advantage of artificial intelligence to detect and classify the femoral shaft fractures.

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