ArticleLife (Basel, Switzerland)2022
Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network.
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Trial
- Review
- Accurate classification and prediction of knee osteoarthritis based on Al-Biruni Earth Radius metaheuristic optimizer and LSTM classifier.Scientific reports · 2026Article
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- Quantifying features from X-ray images to assess early stage knee osteoarthritis.Medical & biological engineering & computing · 2025Article
- Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2025Review
- A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays.Biomedical engineering letters · 2025Review
- MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning.Life (Basel, Switzerland) · 2023Article
- Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted.Diagnostics (Basel, Switzerland) · 2023Article
- Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach.Healthcare (Basel, Switzerland) · 2023Article
- Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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Abstract
Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.
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