Title: Knee osteoarthritis using hybrid deep learning approach with SqueezeNet and ResNet

Authors: A. Muthukumar; S. Singaravelan; R. Arun; V. Selvakumar; D. Arun Shunmugam; S. Balaganesh; P. Gopalsamy

Addresses: Department of CSE, P.S.R. Engineering College, Sivakasi, India ' Department of CSE, P.S.R. Engineering College, Sivakasi, India ' Department of CSE, P.S.R. Engineering College, Sivakasi, India ' Department of CSE, P.S.R. Engineering College, Sivakasi, India ' Department of ECE, P.S.R. Engineering College, Sivakasi, India ' Department of CSE, P.S.R. Engineering College, Sivakasi, India ' Department of CSE, P.S.R. Engineering College, Sivakasi, India

Abstract: Osteoarthritis is a degenerative joint sickness that influences a huge number of individuals around the world. Early location and determination of osteoarthritis is basic for viable treatment and the board of the illness. As of late, warm imaging has arisen as a promising painless procedure for identifying osteoarthritis. In any case, existing methods for osteoarthritis recognition in warm pictures experience the ill effects of a few constraints, like low precision, restricted generalisability, and absence of interpretability. To address these difficulties, we propose an original methodology for osteoarthritis recognition in warm pictures utilising the half and half ResNet-SqueezeNet model profound learning design. The proposed approach includes pre-handling the warm pictures to improve their highlights, trailed by division to extricate the area of interest.

Keywords: ResNet; SqueezeNet; osteoarthritis; hyperparameters; nitty gritty investigation.

DOI: 10.1504/IJCVR.2026.153129

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.3, pp.347 - 359

Received: 11 Jan 2024
Accepted: 04 Feb 2024
Published online: 23 Apr 2026 *

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