Bone Fracture Detection Based on Deep Learning Techniques

Accurate and timely diagnosis of bone fractures is vital to administer appropriate medical treatment; however, the conventional methods based on manual analysis of X-ray images are susceptible to errors and variations. A comprehensive framework for the automatic categorization of bone fractures is outlined in this paper, encompassing both both sophisticated deep learning methods and conventional machine learning procedures. It tests a range of models from ResNet, DenseNet, VGG19, EfficientNet, Vision Transformers, and even some hybrid architectures such as ResNet-Hybrid and DenseNet-Hybrid using a custom-built CNN. The methodology suggested is preprocessing, augmentation, and transfer learning for the improvement of the model performance. Though ResNet and DenseNet are known to extract features very strongly, the best model was the DenseNet-Hybrid that attained near-perfect accuracy because of its domain-specific adaptations. Vision Transformers capture more global relationships in images compared to EfficientNet, which has a computationally efficient model. VGG19 forms a strong baseline for extracting features, and the user-defined CNN provides a personal approach that yields competitive accuracy levels. Traditional approaches, such as SVM, have low levels of accuracy because they base their features on manual definitions. This finds support in the potential for hybrid and advanced deep learning models to revolutionize fracture detection with improved accuracy and efficiency within clinical settings and leads to better patient outcomes.