Automated Classification of Bone Fractures using Deep Learning

Accurate classification of osteoporosis caused by sports injury, accident, or osteoporosis is important for successful treatment and rehabilitation. However, inherent complexity and variation in fracture types, bone structure, and imaging status tend to result in error-prone manual interpretation of X-rays, which may limit the use of relevant medical interventions in time has been long gone The proposed method combines non-local extraction and wavelet-based decomposition tools for advanced preprocessing, ensuring that the image input is free of noise artifacts DenseNet, which is used for model training and transfer learning , is a backbone architecture for feature extraction and classification. This method achieves an impressive accuracy of 86%, which demonstrates the robustness and reliability of the fracture detection and classification tasks. The most important contribution of this study is the integration of publicly available data from Kagle with real-world clinical data from the Government Primary Health Center in Bamori, Guna (MP). This combination of data sets increases model generalizability and diagnostic accuracy in different imaging scenarios. Furthermore, the study highlights the importance of reducing human error in asthma diagnosis, and maximizing diagnostic speed, especially in resource-limited settings where access to radiologists may be restricted emphasizing the training. The results of this study highlight the transformative potential of deep learning in health, providing a scalable and effective solution for automated fracture detection. Future work aims to further refine the model by adding focusing to detect fractures of particles or particles and to extend the framework to other types of medical imaging applications.