Hybrid Approach for Osteoarthritis Detection in X-Ray Images Using SqueezeNet and Grey Wolf Optimizer

Author(s): Singaravelan Shanmugasundaram (PSR Engineering College, India), A. Muthukumar (PPG Institute of Technology, India), Judith Varshini J. C. (Sri Eshwar College of Engineering, India), D. Murugan (Manonmaniam Sundaranar University, India), Gomathy Nayagam M (Ramco Institute of Technology, India), Priskilla Angel Rani J. (Francis Xavier Engineering College, India), P. Gopalsamy (PSR Engineering College, India)and S. Balaganesh (PSR Engineering College, India)
Copyright: 2025
Pages: 22
EISBN13: 9798337355610

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Abstract

In recent years, X ray imaging has emerged as a promising non-invasive technique for detecting osteoarthritis. However, existing techniques for osteoarthritis detection in thermal images suffer from several limitations, such as low accuracy, limited generalizability, and lack of interpretability. To address these challenges, we propose a novel approach for osteoarthritis detection in x ray images using the SqueezeNet model deep learning architecture. The proposed approach involves pre-processing the X-ray images to enhance their features, followed by segmentation to extract the region of interest. The segmented region is then fed into the SqueezeNet model, which is trained to classify the thermal image as normal or abnormal based on the presence of osteoarthritis. The parameter of SqueezeNet is tuned using grey wolf optimizer to reach maximum accuracy. We evaluated the performance of the proposed approach on a dataset of thermal images collected from patients with osteoarthritis and healthy controls.

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Hybrid Approach for Osteoarthritis Detection in X-Ray Images Using SqueezeNet and Grey Wolf Optimizer

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