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A Perspective on Intelligent Imaging Techniques for Early Prediction of Knee Osteoarthritis
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Author(s): Binit Roy (ICFAI University, Tripura, India)and Debapriya Banik (ICFAI University, Tripura, India)
Copyright: 2025
Pages: 22
Source title:
Signal and Image Processing Techniques for Defense, Security, and Healthcare
Source Author(s)/Editor(s): B. Omkar Lakshmi Jagan (Vignan's Institute of Information Technology, India), Amrit Mukherjee (University of South Bohemia, Czech Republic), Thayyaba Khatoon Mohammed (Malla Reddy University, India)and Vustikayala Sivakumar Reddy (Malla Reddy University, India)
DOI: 10.4018/979-8-3693-3840-7.ch001
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Abstract
A serious skeletal illness that lowers well-being and increases fracture risk, osteoporosis affects a large number of people worldwide. Early detection of osteoporosis in the knee is essential for successful treatment. This study presents deep learning, machine learning, and image processing techniques. It entails taking images of the knee joint using thermal imaging, x-rays, or other techniques, then processing them to improve quality and standardize format. The knee region is the target of methods such as region-of-interest (ROI) extraction. Numerous images are examined, encompassing geometric dimensions, bone mineral density, and textural features. Predictive models, such as decision trees, support vector machines (SVM), and convolutional neural networks (CNNs), classify knee joints as osteoporotic or healthy. The study also explores using an ensemble of multiple machine-learning models to improve system robustness and effectiveness.
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