Evaluating the Efficacy of Convolutional Neural Networks for Bone Fracture Detection from X-ray Images

Hiren Mewada, L. Syam Sundar, Nishant Kumar

This paper presents a thorough analysis of the application of convolutional neural networks (CNNs) for sophisticated X-ray fracture classification. Traditional fracture detection and classification methods frequently suffer from limited accuracy and effectiveness, necessitating the investigation of more sophisticated approaches. With their exceptional ability to automatically identify and acquire new characteristics from imaging data, CNNs have become a powerful tool in the field of medical diagnosis. This paper presents open-sourced datasets and their characteristics for bone fracture images, which play a crucial role in classification algorithms. Then, the paper showcases the most recent developments in CNN-based fracture classification, demonstrating gains in speed and accuracy of diagnosis. This work analyzes the resilience, accuracy, and performance of many CNN architectures used for X-ray fracture classification. It is observed that ResNet and ensemble methods demonstrated superior performance relative to conventional CNNs and machine learning algorithms, attaining a maximum accuracy of 94%. Nevertheless, these models are computationally demanding. A significant limitation is that the generalization of these models has not been thoroughly evaluated, which represents a major weakness. Finally, the potential benefits of CNN technology in clinical settings, emphasizing its potential to enhance patient care through faster and more precise diagnosis, have been discussed.