Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Basak, Anushka; Kumar, Ashish; Behera, Gitika; Impana K, P.; Basak, Reshmi Title A name given to the resource OrthoTrace - Fracture Detection from X-Ray Images Using YOLOv5 Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Conference on Communication, Computer and Information Technology, IC3IT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC3IT66137.2025.11341605" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3IT66137.2025.11341605</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033364819?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033364819?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Deep Learning; Flask; Fracture Detection; Medical Image Analysis; Xray Imaging; YOLOv5 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152483-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online