Enhanced Bone Fracture Detection and Quantification in X-Ray Images Using Deep Learning

Aman Kshetri, Raj Sah Rauniyar, S S Chakravarthi

2025

Abstract

Bone fracture detection in X-ray imaging is an essential diagnostic process, yet it often requires specialized expertise that may be limited in under-resourced healthcare settings. In major hospitals, experienced radiologists typically interpret X-rays with high accuracy. However, in smaller facilities within underdeveloped regions, less experienced medical personnel may struggle to provide accurate readings, leading to a significant rate of misinterpretation, currently reported at 26%. While numerous studies have focused on localizing fractures, few address the need for quantifying the length of the fractured bone segment, a critical factor in treatment planning. This project aims to develop an advanced deep learning model using the YOLO architecture to enhance bone fracture detection and quan-tification in X-ray images. By automating fracture detection and accurately measuring fracture length, the YOLO-based model will improve diagnostic accuracy, reduce radiologist workload, and ensure consistent assessments across diverse healthcare environments. The objectives include designing robust algorithms for fracture localization and length measurement, achieving high precision in fracture detection, and validating the model against a comprehensive X-ray dataset. Ultimately, this tool is expected to provide valuable diagnostic aid, particularly in settings with limited radiological resources, improving patient outcomes through reliable, automated fracture analysis.

Paper Citation

in Harvard Style

Kshetri A., Sah Rauniyar R. and S Chakravarthi S. (2025). Enhanced Bone Fracture Detection and Quantification in X-Ray Images Using Deep Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 80-86. DOI: 10.5220/0013586900004664

in Bibtex Style

@conference{incoft25,
author={Aman Kshetri and Raj Sah Rauniyar and S S Chakravarthi},
title={Enhanced Bone Fracture Detection and Quantification in X-Ray Images Using Deep Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={80-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013586900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Enhanced Bone Fracture Detection and Quantification in X-Ray Images Using Deep Learning
SN - 978-989-758-763-4
AU - Kshetri A.
AU - Sah Rauniyar R.
AU - S Chakravarthi S.
PY - 2025
SP - 80
EP - 86
DO - 10.5220/0013586900004664
PB - SciTePress