OrthoTrace - Fracture Detection from X-Ray Images Using YOLOv5

Title

OrthoTrace - Fracture Detection from X-Ray Images Using YOLOv5

Creator

Basak, Anushka; Kumar, Ashish; Behera, Gitika; Impana K, P.; Basak, Reshmi

Description

Rapid and correct diagnosis of bone fractures is important for appropriate and efficient management in clinical practice; however, traditional X-ray interpretation frequently involves diagnostic error, particularly in low resource environments. This study presents OrthoTrace, a YOLOv5-based deep learning system designed for real-time and lightweight fracture detection. The model was trained on a curated set of annotated X-ray images from the publicly available YOLOv5 dataset, which includes a range of upper and lower limb fractures across diverse anatomical regions regions ( 3,500 - 4,000 images; 70:20:10 train/validation/test split) and evaluated using accuracy, mean Average Precision (mAP), precision, recall, and F1-score. On the held-out test set, OrthoTrace achieved 90% accuracy and an mAP of 0.91, outperforming standard CNN baselines and approaching YOLOv8 performance while requiring significantly fewer computational resources. YOLOv5 can also identify fractures in real time, and draw bounding boxes with confidence scores around the specific areas of the X-ray with fractures. OrthoTrace can also be deployed in local or cloud environments. The novelty of OrthoTrace lies in its incredibly rapid inference, while requiring very little computing power. Although promising, the system is limited by dataset size and lack of external validation. This system holds clinical significance as it can support rapid, reliable fracture detection in low-resource or emergency settings, making real-time deployment feasible on standard hospital hardware. Future work will focus on expanding datasets, and enhancing robustness across various medical scenarios. 2025 IEEE.

Source

International Conference on Communication, Computer and Information Technology, IC3IT 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Deep Learning; Flask; Fracture Detection; Medical Image Analysis; Xray Imaging; YOLOv5

Coverage

Basak A., Jss Academy of Technical Education, Dept. of Computer Science and Engineering, Bengaluru, India; Kumar A., Jss Academy of Technical Education, Dept. of Computer Science and Engineering, Bengaluru, India; Behera G., Jss Academy of Technical Education, Dept. of Computer Science and Engineering, Bengaluru, India; Impana K P., Jss Academy of Technical Education, Dept. of Computer Science and Engineering, Bengaluru, India; Basak R., Christ University, Dept. of Mechanical and Automobile Engineering, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152483-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IC3IT66137.2025.11341605

https://www.scopus.com/pages/publications/105033364819?origin=resultslist