International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The recognition and categorization of bone fractures hold great significance in urgent medical care. It determines how practitioners make critical decisions and aids in preventing delays that might jeopardize patient safety. Manually interpreting X-rays, CT scans, and MRIs can be time-consuming, and honestly, human reviewers can overlook crucial details, especially when it comes to tiny or intricate fractures. However, deep learning has revolutionized this landscape. Automated systems now rapidly identify fracture patterns with remarkable precision. In this research, we examine how convolutional neural networks (CNNs), transfer learning, and hybrid deep learning frameworks can elevate our ability to detect fractures. We train and evaluate these models using medical images that we have pre-processed think data augmentation, image enhancement, and feature extraction. This helps the models generalize more effectively and identify fractures that may be less evident. The objective is to classify fractures based on type, severity, and location, enabling physicians to initiate appropriate treatment promptly. Our findings reveal that deep learning models surpass traditional machine learning methods, achieving higher sensitivity and specificity across diverse datasets. AI-driven tools can significantly boost radiologists’ efficiency, alleviate their workload, and facilitate quicker, better care for patients. Looking forward, there's an opportunity to incorporate additional data types, create real-time systems, and enhance understanding of AI, Deep Learning, and Machine Learning decisions, thus making these tools even more dependable for everyday clinical applications.

Keywords: Bone fracture identification, Deep learning, Convolutional neural networks (CNN), Medical image categorization, X-ray evaluation, Transfer learning, Computer-aided diagnosis (CAD), Medical imaging.

Downloads: | DOI: 10.17148/IJARCCE.2025.1411144