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@article{172211,
        author = {Shreyas D and Shrinidhi S Hegde and Roopitha G Nayak and Nithin S and Dr K R Shylaja},
        title = {Osteoporosis Detection in Spine using Deep Learning Methods},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2287-2293},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172211},
        abstract = {The proposed model "Osteoporosis Detection in Spine using Deep Learning Methods" aims to enhance the diagnosis of osteoporosis by integrating machine learning and deep learning techniques. Traditional bone mineral density (BMD) assessments are insufficient, achieving only a 50% success rate in predicting vertebral fragility fractures (VFF). This project uses a representative dataset of spine DXA Scans and compares various models, including deep learning architectures like CNN, VGG16, and ResNet. The hybrid model obtained by ensembling ResNet with RF Classifier, CNN and VGG16 demonstrated superior performance in identifying patients at risk of VFF. The results highlight significant advancements in accuracy, laying the groundwork for future studies that can improve osteoporosis risk prediction and aid clinical decision-making with better tools for early intervention.},
        keywords = {},
        month = {January},
        }