A Sequential VGG16+CNN-Based Automated Approach With Adaptive Input for Efficient Detection of Knee Osteoarthritis Stages

Osteoarthritis (OA) is a prevalent musculoskeletal disorder, predominantly affecting the knee joint and resulting in significant pain and functional limitations. The Kellgren-Lawrence (KL) grading system, traditionally utilized by radiologists, assesses OA severity through radiographic evaluation of...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.62407-62415
Main Authors: Rehman, Shafiq Ur, Gruhn, Volker
Format: Article
Language:English
Subjects:
Arthritis
Artificial neural networks
Automation
Bones
Computational modeling
convolutional neural network (CNN)
Convolutional neural networks
Data augmentation
Kellgren and Lawrence grading
Knee
Knee osteoarthritis
Model accuracy
Neural networks
Osteoarthritis
Pain
ResNet50
Solid modeling
VGG16/VGG19
X-Ray
X-rays
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Summary:Osteoarthritis (OA) is a prevalent musculoskeletal disorder, predominantly affecting the knee joint and resulting in significant pain and functional limitations. The Kellgren-Lawrence (KL) grading system, traditionally utilized by radiologists, assesses OA severity through radiographic evaluation of knee bones on both sides. Recent advancements in computer-aided methods have focused on enhancing diagnostic precision and efficiency, leveraging automated classification models with Knee X-rays which represent a cornerstone imaging modality for OA severity assessment. In this study, a novel hybrid model combining Convolutional Neural Networks (CNN) and VGG16 architectures is proposed to achieve accurate OA detection. Various neural networks, including CNN, VGG16, VGG19, ResNet50, and CNN-ResNet, are implemented and compared alongside the proposed method. Additionally, data augmentation techniques are employed to address class imbalance, leading to enhanced accuracies across all models. Analysis demonstrates robust performance of all models on the training set. The proposed hybrid method (CNN-ResNet50) outperforms other state-of-the-art approaches, providing precise results across all five stages of OA according to the KL grading system by achieving an accuracy exceeding 93% on training, validation, and testing datasets.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3395062