DETECTION OF BONE FRACTURES IN MEDICAL IMAGES USING COMPUTER VISION

Abstract

Accurate detection of bone fractures is critical for timely diagnosis, effective treatment, and patient recovery. Traditional manual assessment of radiographs is time-consuming and subject to inter-observer variability, which can delay appropriate intervention. Computer vision (CV) and artificial intelligence (AI) technologies have emerged as powerful tools for automated fracture detection, enabling rapid, reliable, and precise analysis of medical images. This paper provides an overview of computer vision-based approaches for bone fracture detection, focusing on deep learning models, convolutional neural networks (CNNs), and hybrid algorithms. Performance evaluation, clinical applicability, challenges such as data variability and limited annotated datasets, and future directions are discussed. The study highlights the potential of AI-driven computer vision systems to enhance diagnostic accuracy, support radiologists, and improve patient care.