Automatic Detection of Knee Osteoarthritis Disease with the Developed CNN, NCA and SVM Based Hybrid Model
Bibliographic record
Abstract
Knee osteoarthritis (Knee-OA) is one of the most common musculoskeletal diseases caused by loss of cartilage and bone changes in the joint.Prediction of early Knee-OA based on early bone tissue analysis is challenging in medical image analysis.If the disease is detected in the later stages, it may cause serious problems, such as the need for knee replacement.Therefore, the detection of Knee-OA disease is essential.With the developing technology, computer-aided systems have been frequently used in the biomedical field in recent years.A deep learning-based hybrid model for the early diagnosis and treatment of Knee-OA disease was developed in this study.In the developed hybrid model, three different CNN architectures were used as the base, and feature extraction was made with these architectures.The features obtained in three different architectures are combined to bring together different features of the same image.After merging, the neighboring component analysis (NCA) size reduction method was used to remove unnecessary features.Since unnecessary features are eliminated from the feature map optimized with NCA, the proposed hybrid model will work faster and produce more successful results.Finally, the feature map optimized with NCA was classified with six different classifiers.The proposed model was also compared to eight different CNN architectures.In comparison to CNN architectures, the proposed hybrid model achieved the highest accuracy performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".