# Detection of distal radius fractures trained by a small set of X-ray images and Faster R-CNN

## TL;DR

This study demonstrates that a Faster R-CNN neural network can accurately detect distal radius fractures in X-ray images using only a small dataset of 38 images, surpassing physician accuracy.

## Contribution

The paper introduces a method for effective fracture detection with minimal training data, highlighting its potential for rare disease diagnosis.

## Key findings

- Achieved 96% accuracy in fracture detection
- Mean Average Precision of 0.866
- Outperformed physicians in detection accuracy

## Abstract

Distal radius fractures are the most common fractures of the upper extremity in humans. As such, they account for a significant portion of the injuries that present to emergency rooms and clinics throughout the world. We trained a Faster R-CNN, a machine vision neural network for object detection, to identify and locate distal radius fractures in anteroposterior X-ray images. We achieved an accuracy of 96\% in identifying fractures and mean Average Precision, mAP, of 0.866. This is significantly more accurate than the detection achieved by physicians and radiologists. These results were obtained by training the deep learning network with only 38 original images of anteroposterior hands X-ray images with fractures. This opens the possibility to detect with this type of neural network rare diseases or rare symptoms of common diseases , where only a small set of diagnosed X-ray images could be collected for each disease.