Severity Classification of Alzheimer Dementia Based on MRI Images Using Deep Neural Network
Bibliographic record
Abstract
Alzheimer's dementia (AD) is the most common type of dementia, usually characterized by memory loss followed by progressive cognitive decline and functional impairment. AD is one of the leading causes of death and cannot be cured, but proper medical treatment can delay the severity of the disease. Early detection of AD can detect early and prevent the disease from getting worse. So, we need a system that can detect AD as a means of support for the clinical diagnosis. In this study, a system was designed to classify the severity of AD using the Convolutional Neural Network (CNN) method with VGG-16 and VGG-19 modeling. From the simulation results with a total of 4,160 MRI datasets, the highest accuracy rate was 98.28% with VGG-19 architecture using Adam's Optimizer for the classification of 3 classes, namely no dementia (normal), mild dementia, and moderate dementia. It is hoped that this study can support clinical diagnosis in assessing the severity of AD.
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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 | 0.000 |
| 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 | 0.000 |
| 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".