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Evaluate the effectiveness of SCSNet in classifying the severity of knee osteoarthritis using medical imaging.

January 6, 20260 citationsOpen Access

Automated Detection and Severity Classification of Knee Osteoarthritis Using a Scalable Convolutional Neural Network

JTJournal of Theoretical and Applied Information Technology

Key Points

Evaluate the effectiveness of SCSNet in classifying the severity of knee osteoarthritis using medical imaging.
Developed a deep learning model (SCSNet) for classifying knee osteoarthritis severity using X-ray images.
Categorized knee osteoarthritis into five groups: Minimum, Healthy, Moderate, Doubtful, and Severe.
Compared SCSNet performance with three pre-trained models: VGG-16, ResNet-50, and Xception.
SCSNet achieved 98% accuracy, outperforming other models in all evaluated metrics.
Precision, recall, and F1 score were employed to assess model performance.

Abstract

Osteoarthritis of the knee is a common degenerative joint condition that lowers quality of life and causes disability, especially in people over 60. It is brought on by the knee joint's degenerating cartilage, which causes bone-on-bone contact, discomfort, stiffness, swelling, and restricted movement. Deep neural networks, specifically “CNNs (Convolutional Neural Networks)”, have shown a lot of promise in medical image processing for the identification and classification of diseases. In order to categorize knee osteoarthritis utilizing X-ray pictures into five groups—Minimum, Healthy, Moderate, Doubtful, and Severe—this research presents SCSNet, a deep learning model. Precision, recall, F1 score, and accuracy had been employed to compare the model's performance to three pre-trained transfer learning models: “VGG-16”, “ResNet-50”, and “Xception”. According to experimental results, SCSNet outperformed the transfer learning models in every metric assessed, achieving higher performance with 98% accuracy.

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