Classification of Alzheimer’s Disease from MRI Data Using an Ensemble of Hybrid Deep Convolutional Neural Networks
Bibliographic record
Abstract
Although there is no cure for Alzheimer's disease (AD), an accurate early diagnosis is extremely important for both the patient and social care, and it will become even more significant once disease-modifying agents are available to prevent, cure, or even slow down the progression of the disease. In recent years, classification of AD through deep learning techniques has been one of the most active research areas in the medical field. However, most of the existing techniques cannot leverage the entire spatial information; hence, they lose the inter-slice correlation. In this paper, we propose a novel classification algorithm to discriminate patients having AD, mild cognitive impairment (MCI), and cognitively normal (CN) using an ensemble of hybrid deep learning architectures to leverage a more complete spatial information from the MRI data. The experimental results obtained by applying the proposed algorithm on the OASIS dataset show that the performance of the proposed classification framework to be superior to that of the some conventional methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".