A Hybrid Deep Learning and Multiclass SVM Approach for Alzheimer’s Disease Stage Classification from MRI Scans
Bibliographic record
Abstract
Alzheimer’s disease is a progressive neurodegenerative disorder and the most common cause of dementia, leading to memory loss and cognitive decline. Early diagnosis is essential to improve patient outcomes, yet remains difficult due to subtle structural changes in the brain during the early stages of the disease. This study introduces a hybrid deep learning and machine learning framework for classifying different stages of Alzheimer’s disease using four-class OASIS-1 MRI scan data. The proposed method extracts discriminative features using the ConvNeXt-Base model from both cropped brain regions and segmented tissue images (gray matter, white matter, cerebrospinal fluid). These features are then classified using multiclass Support Vector Machines, applying One-vs-One and One-vs-Rest strategies. Our approach achieves high classification performance and demonstrates strong potential for improving early-stage detection and stage-specific diagnosis of Alzheimer’s disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".