ArticleDiagnostics (Basel, Switzerland)2023
Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 91 citations in OpenAlex.
- MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays.Scientific reports · 2026Article
- Accurate classification and prediction of knee osteoarthritis based on Al-Biruni Earth Radius metaheuristic optimizer and LSTM classifier.Scientific reports · 2026Article
- Multimodal model integrating ultrasound and demographic data for the diagnosis of knee osteoarthritis.BMC medical imaging · 2026Article
- Hybrid Ensemble Model for Knee Osteoarthritis Grading: Integrating CNNs with GLCM Features and XAI.Diagnostics (Basel, Switzerland) · 2026Article
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in Kellgren-Lawrence grading of knee osteoarthritis: bridging radiographic tradition with algorithmic precision.Therapeutic advances in musculoskeletal disease · 2026Review
- Fractal gradient divergence-tuned deep belief network for osteoporosis detection using X-ray images.Frontiers in artificial intelligence · 2026Article
- Quantifying features from X-ray images to assess early stage knee osteoarthritis.Medical & biological engineering & computing · 2025Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 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
- Optimizing CNN-Based Diagnosis of Knee Osteoarthritis: Enhancing Model Accuracy with CleanLab Relabeling.Diagnostics (Basel, Switzerland) · 2025Article
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Article
- A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays.Biomedical engineering letters · 2025Review
- CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis.Journal of pathology informatics · 2024Article
- Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique.Scientific reports · 2024Article
- Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2024Article
- OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging.Frontiers in bioengineering and biotechnology · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren-Lawrence (KL) system. This requires the physician's expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.