Enhancing Bone Fracture Classification in X-Ray Using Deep Learning Models

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 303-310, March 2026.

https://doi.org/10.58482/ijersem.v2i3.39

This work is licensed under a Creative Commons Attribution 4.0 International License.

Enhancing Bone Fracture Classification in X-Ray Using Deep Learning Models

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K Maheswari

Neha Kumari

Soha Parween

Aditya Narayan

Ashik Kumar

1Assistant Professor, Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, India.

2-5UG Scholar, Department of CAD, Siddharth Institute of Engineering & Technology, Puttur, India.

Abstract: This paper suggests improvements in the bone fracture classification of the X-ray images using a deep learning model. Accurate classification of fractures is of the utmost importance in medicine, and this paper investigates various possibilities to improve the classification performance. The models using VGG19 with Random forest model, MobileNet Random forest with SVM, Random Forest, Efficient-net with SVM, and XGBoost. Each model is made and tested, which leads to the fact that physicians know how well they will be able to make a correct classification of the different kinds of bone fractures. By comparing both models, the paper brings out the pros and cons of both (pure architectures and hybrid architectures). A simple frontend UI implementation is done using the web technologies (using the web navigation platform, including HTML, CSS, and JS) so that the user can interact with the classification system and visualise the predictions. The application is not meant to be deployed, but to help visualize the workings of deep learning in medical imaging. This work provides insight into the effectiveness of the combination of methods with deep learning and classical machine learning based techniques for an improved outcome of image classification for healthcare applications.

Keywords: Deep Learning, Bone Fracture, X-Ray Analysis, VGG19, MobileNet.

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This article is part of Volume 2, Issue 3 (March 2026)