BoneNet: Bone Fracture Detection Using Deep Learning with Advancing Speed and Accuracy in X-Ray Analysis

This study reports BoneNet, an advanced deep learning framework particularly tailored to classify accurately the condition of bone with high-resolution capabilities in the assessment of bone fracture diagnosis. Bone-related pathologies critically necessitate early detection; hence, two personalized models, BoneNetV1 and BoneNetV2, were developed, engineered in such a manner that would efficiently distinguish and classify structures of bones in medical imaging. BoneNetV1 is developed using the architecture with Conv2D and LeakyReLU layers under a multiple-layer convolutional scheme, and its testing accuracy is reported to be 95.54%. BoneNetV2 combines more depth and optimized layer configurations which enhance the testing accuracy up to 99.12%. These models are highly tested by comparison with the conventional transfer learning models—VGG16, VGG19, ResNet50, DenseNet, InceptionV3, MobileNet, EfficientNet and Xception—showing that BoneNet greatly outperforms these models in metrics precision, recall, and F1 scores. The novelty of BoneNet lies in its architecture specially designed to capture intricate features of bone structure with advanced accuracy and reliability over classical approaches. Such findings put BoneNet as a powerful and reliable solution for the automated detection of bone condition, thus placing it in the potential role of aiding early and precise diagnostic practices in clinical application.