Deep Learning based Method for Alzheimer’s Disease Stages Classification using MRI Images
Bibliographic record
Abstract
Alzheimer’s disease, one of the numerous forms of dementia, presents a considerable challenge to medical care systems. Indeed, there is currently no cure, but early diagnosis and prevention of the disease might be the consequence of ineffective treatment. The absence of effective treatments has led many scientists to look for other ways to analyze and detect cases at a premature stage. One of the ways that are receiving considerable interest is the one based on deep learning, which enables computers to learn from massive datasets without requiring human supervision. This has allowed the development of algorithms with high accuracy leading to better results than traditional methods when used with a doctor’s medical evaluation. This paper focuses on developing a technique based on a Convolutional Neural Network to classify Alzheimer’s disease stages from Magnetic Resonance Imaging data through two distinct scenarios. We compared our results with other state-of-the-art methods, and ours yielded more promising performances.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".