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@article{175166,
        author = {J S Bhavana and C Rohitha reddy and A Dillipriya and E Mounika and Mrs.U Chandeepriya and Mr.Pandreti Praveen},
        title = {Bone fracture detection and classification using deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1914-1923},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175166},
        abstract = {Timely and accurate classification of bone fractures is essential for effective orthopedic diagnosis and treatment planning. This study proposes a deep learning-based multiclass classification framework for automated bone fracture detection using radiographic images, without the need for segmentation or localization techniques. We evaluate and compare the performance of three state-of-the-art convolutional neural network architectures—DenseNet-121, EfficientNet-B0, and ResNet-50—for classifying ten different fracture types, including comminuted, oblique, spiral, and pathological fractures. The models are trained on a labeled dataset with class balancing and data augmentation strategies to improve generalization. Experimental results demonstrate that DenseNet-121 achieves the highest classification accuracy of 98.94%, followed by EfficientNet-B0 with 98.76%, and ResNet-50 with 98.50%. Evaluation metrics such as precision, recall, and F1-score further confirm the robustness and reliability of the proposed approach. The findings highlight the effectiveness of deep learning models in automated fracture classification, offering a scalable solution to support clinical decision-making and reduce diagnostic workload.},
        keywords = {Bone fracture classification, Deep learning, DenseNet-121, EfficientNet-B0, ResNet-50, Multiclass classification, Radiographic image analysis, Computer-aided diagnosis (CAD), Convolutional neural networks (CNNs), Medical image classification.},
        month = {April},
        }