A Deep Learning Pipeline for Classifying Different Stages of Alzheimer's Disease from fMRI Data.
Bibliographic record
Abstract
Abstract \n \nAlzheimer’s disease (AD) is an irreversible, progressive neurological disorder that causes \nmemory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages \nof AD. However, the similarity of the brain patterns in older adults and in different stages \nmakes the classification of different stages a challenge for researchers. \n \nIn this thesis, convolutional neuronal network architecture AlexNet was applied to \nfMRI datasets to classify different stages of the disease. We classified five different stages \nof Alzheimer’s using a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer’s disease (AD). The model \nwas implemented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed to avoid any noise. Then, low to high \nlevel features were extracted and learned using the AlexNet model. Our experiments \nshow significant improvement in classification. The average accuracy of the model was \n97.63%. We then tested our model on test datasets to evaluate the accuracy of the model \nper class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, \n98.34% for NC, and 94.55% for SMC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".