Authors:
Aman Kshetri
;
Raj Sah Rauniyar
and
S S Chakravarthi
Affiliation:
Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India
Keyword(s):
Bone fracture detection, X-ray Imaging, YOLO, Radiologists, Deep Learning.
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 e
nsure 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.
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