Bone Fracture Classification using Transfer Learning

TL;DR

This paper presents a transfer learning approach for bone fracture classification in X-ray images, achieving high accuracy quickly and emphasizing responsible AI practices and dataset quality.

Contribution

Introduces a robust training loop for fracture classification that outperforms existing methods with fewer epochs and highlights dataset quality and responsible training.

Findings

Achieves superior performance in less than ten epochs

Utilizes the latest dataset for optimal results

Emphasizes responsible AI and dataset quality

Abstract

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.