A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis
Abstract Knee osteoarthritis (KOA) is a severe arthrodial joint condition with significant global socioeconomic consequences. Early recognition and treatment of KOA is critical for avoiding disease progression and developing effective treatment programs. The prevailing method for knee joint analysis...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-99460-4 |
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| Summary: | Abstract Knee osteoarthritis (KOA) is a severe arthrodial joint condition with significant global socioeconomic consequences. Early recognition and treatment of KOA is critical for avoiding disease progression and developing effective treatment programs. The prevailing method for knee joint analysis involves manual diagnosis, segmentation, and annotation to diagnose osteoarthritis (OA) in clinical practice while being highly laborious and a susceptible variable among users. To address the constraints of this method, several deep learning techniques, particularly the deep convolutional neural networks (CNNs), were applied to increase the efficiency of the proposed workflow. The main objective of this study is to create advanced deep learning (DL) approaches for risk assessment to forecast the evolution of pain for people suffering from KOA or those at risk of developing it. The suggested methodology applies a collective transfer learning approach for extracting accurate deep features using four pre-trained models, VGG19, ResNet50, AlexNet, and GoogleNet, to extract features from KOA images. The numeral of extracted features was reduced for identifying the most appropriate feature attributes for the disease. The binary Greylag Goose (bGGO) optimizer was employed to perform this task, with an average fitness of 0.4137 and a best fitness of 0.3155. The chosen features were categorized utilizing both deep learning and machine learning approaches. Finally, a CNN hyper-parameter algorithm was performed utilizing GGO. The suggested model outperformed previous models with accuracy, sensitivity, and specificity of 0.988692, 0.980156, and 0.990089, respectively. A comprehensive statistical analysis test was performed to confirm the validity of our findings. |
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| ISSN: | 2045-2322 |