# Bone Fracture Classification using Transfer Learning

arXiv: https://arxiv.org/abs/2406.15958 · HTML: https://mlsanity.com/paper/2406.15958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation](https://mlsanity.com/paper/2012.04066/) (2020)
- [Long-Bone Fracture Detection using Artificial Neural Networks based on Line Features of X-ray Images](https://mlsanity.com/paper/1902.07458/) (2019)

## Abstract (arXiv)

The manual examination of X-ray images for fractures is a time-consuming process that is prone to human error. In this work, we introduce a robust yet simple training loop for the classification of fractures, which significantly outperforms existing methods. Our method achieves superior performance in less than ten epochs and utilizes the latest dataset to deliver the best-performing model for this task. We emphasize the importance of training deep learning models responsibly and efficiently, as well as the critical role of selecting high-quality datasets.