Automated Detection of Alzheimer’s Disease and Mild Cognitive Impairment Using Whole Brain MRI
Bibliographic record
Abstract
Early diagnosis is critical for the development and success of interventions, and neuroimaging is one of the most promising areas for early detection of Alzheimer’s disease (AD). This study is aimed to develop a deep learning method to extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI) and cognitively normal (CN) groups. In this work, we adapted and trained convolutional neural networks (CNNs) on sMRI images of the brain from ADNI datasets available in online databases. Our proposed mechanism was used to combine features from different layers to hierarchically transform the images from magnetic resonance imaging into more compact high-level features. The proposed method has reduced number of parameters which reduces the computation complexity. The method is compared with the existing state-of-the-art works for AD classification, which show superior results for the widely used evaluation metrics including accuracy, area under the ROC curve etc., suggesting that our proposed convolution operation is suitable for the AD diagnosis.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".