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@article{163401,
        author = {Ponni S},
        title = {Detection of Osteoporosis and Osteoarthritis Using Deep Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {1160-1167},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163401},
        abstract = {Osteoporosis and osteoarthritis in the knee are common musculoskeletal conditions that have significant consequences for healthcare. The possibility of deep learning techniques to automate the detection of these circumstances is investigated in this work. For the analysis of medical images (such as X-rays and CT scans) and clinical data, we implement and compare the performance of various deep learning architectures, including Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), Multilayer Perceptrons (MLPs), and Long Short-Term Memory (LSTM) networks. Important performance indicators like accuracy, loss, sensitivity, Receiver Operating Characteristics (ROC), Area Under the Curve (AUC), and Scalability, are utilized to assess each approach's effectiveness. The results were impressive, with CNN demonstrating exceptional accuracy in both diagnoses. For osteoporosis detection, CNN achieved a remarkable 94.3% accuracy, significantly outperforming ANN (87%), MLP (80%), and LSTM (74%). In osteoarthritis detection, CNN again displayed dominance with a 99% accuracy rate, followed by ANN (88.7%), MLP (87%), and LSTM (80.3%). This comprehensive study strongly suggests that deep learning holds immense promise for revolutionizing the diagnosis of Osteoporosis and Osteoarthritis.},
        keywords = {ANN, CNN, Deep Learning, Knee Osteoporosis, Knee Osteoarthritis, LSTM, Medical Image Analysis, MLP

},
        month = {},
        }