Automated Severity Classification of Knee Osteoarthritis from Radiographs Using Transfer Learning Based Deep Neural Networks

Syed Nisar Hussain Bukhari; Sehar Altaf

doi:10.32604/jai.2026.077943

January 1, 2026Journal on artificial intelligenceOpen Access

Automated Severity Classification of Knee Osteoarthritis from Radiographs Using Transfer Learning Based Deep Neural Networks

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Key Points

The study aims to develop an automated system for classifying the severity of knee osteoarthritis from radiographic images using deep learning techniques.
Utilized transfer learning techniques with deep neural networks
Analyzed radiographic images of knee joints
Implemented a classification system to assess severity levels
The automated classification system demonstrated high accuracy
Improved diagnostic capabilities compared to traditional methods
Successfully identified different severity levels with minimal errors

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Implication

Automated approaches classify knee osteoarthritis severity using radiographs, suggesting improved diagnostic accuracy.

Abstract

Knee osteoarthritis is a progressive degenerative joint disorder that leads to pain, stiffness, and reduced mobility, significantly affecting quality of life. Early and reliable diagnosis is essential for effective disease ma... | Find, read and cite all the research you need on Tech Science Press

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Cite This Study

Bukhari et al. (2026) studied this question.

synapsesocial.com/papers/69cd79e15652765b073a6c58 https://doi.org/10.32604/jai.2026.077943

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Automated Severity Classification of Knee Osteoarthritis from Radiographs Using Transfer Learning Based Deep Neural Networks

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Automated Severity Classification of Knee Osteoarthritis from Radiographs Using Transfer Learning Based Deep Neural Networks

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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