An overview of techniques for automatic detection of bone fractures

Abstract

The automatic identification of bone fractures by medical imaging signifies a notable progress in healthcare, enhancing diagnostic precision, alleviating the burden on medical professionals, and expediting patient care. This paper offers a thorough examination of contemporary techniques employed for the automatic detection of bone fractures, emphasizing image processing methods, conventional machine learning algorithms, and cutting-edge deep learning strategies. A systematic literature review methodology was utilized to examine various studies, contrasting the performance, advantages, and limitations of methodologies such as convolutional neural networks (CNN), region-based convolutional networks (R-CNN), and transformer-based architectures. Challenges like data availability, image quality, computational complexity, and integration into clinical operations are thoroughly examined. The report highlights emerging trends and suggests future research directions, emphasizing opportunities in multimodal data integration, explainable artificial intelligence, and federated learning techniques to enhance fracture diagnosis accuracy and reliability.

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