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@article{178993,
        author = {Aditya Bansal and Amit Gupta and Jahanvi Vaish and Latika Sharma},
        title = {Bone Fracture Detection Web Application Using Computer Vision Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7214-7222},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178993},
        abstract = {Bone fractures are a major medical concern re- quiring prompt and precise diagnosis to enhance recovery and reduce long-term complications. Conventional methods depending on manual analysis of X-ray images are labor-intensive and essentially subjective, often leading to delayed intervention. Recent progress in deep learning, especially convolutional neural networks (CNNs), has transformed medical imaging through automated feature extraction, leading to notable improvements in diagnostic accuracy. In this work, we propose a full-stack web- based diagnosis system leveraging three state-of-the-art models yolov8 faster R-CNN with resnet backbone and VGG16 with SSD for automated bone fracture identification and localization to ensure precise results and enable real-time analysis. Our system performs substantial preprocessing aggressive data augmentation and intense hyperparameter optimization. Our solution minimizes radiologist workload and streamlines clinical decision- making paving the way for widespread application of artificial intelligence in diagnosing fractures. Overall this combined solution can transform conventional diagnostic methods in modern clinical practice.},
        keywords = {Deep Learning, Bone Fracture Detection, YOLOv8, Faster R-CNN, VGG16, Medical Image Analysis, Web Application.},
        month = {May},
        }