Early Detection of Alzheimer's Using MRIs and Explainable 3D CNNs
Bibliographic record
Abstract
This paper presents a 3D Convolutional Neural Network (CNN) that can classify stages of Alzheimer's disease, which is a progressive neurodegenerative disorder marked by memory loss and cognitive decline, in both binary and multi-class settings with great accuracy. The Magnetic Resonance Imaging (MRI) images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have been used. Even though many studies report lower accuracy with MRI, our approach demonstrates that a carefully designed preprocessing pipeline and optimized hyperparameter tuning can unlock the true potential of MRI for AD classification outperforming state-of-the-art models. Our final model achieved an average accuracy of 94.37 % in the multiclass classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) and cognitive normal (CN), 91. 79% accuracy in the binary classification of AD vs. CN cases, and 83. 02% accuracy in Early MCI cases vs. CN. In addition, an interpretability occlusion-based technique has been added to these models to highlight the brain regions that contribute most to the model's predictions. This revealed that the most influential regions were part of the extracted regions of interest which are areas known to be critical in AD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 0.001 |
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".