3D CNN Design for the Classification of Alzheimer’s Disease Using Brain MRI and PET
Bibliographic record
Abstract
Attempt to diagnose Alzheimer's disease (AD) using imaging modalities is one of the scopes of deep learning. While considering the theoretical background from past studies, we are trying to identify convolutional neural network (CNN) behaviors moving from 2D to 3D architecture. This study aims to explore the output from a variety of CNN models implemented in the MRI or/and PET classification tasks for AD prediction while trying to summarize its characteristics with a variety of parameters that are tuned and changed. There are many architectures available; however, we are testing a basic architecture with a change in the reception area based on the convolutional layer's kernel size and its strides. The architecture has been categorized as converging, diverging, or equivalent if the filter kernel size is unchanged. This investigation studies a simple encoder based CNN with a sequential flow of features from low-level to high-level feature extraction. The idea is to present a diverging reception area by increasing the filter size and stride from a lower to a higher level. As a result, the feature redundancy is reduced and the trivial features keep on diminishing. The proposed architecture is referred to as `divNet', and several experiments were performed to determine how effective the architecture is in terms of the consumed memory, the number of parameters, running time, classification error, and the generalization error. This study surveys several related experiments by changing the hyper-parameters setting, the architecture selection based on the depth and area of the reception feature, and the data size.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".