Alzheimer’s Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features
Bibliographic record
Abstract
Brain network plays an important role in representing abnormalities in Alzheimers disease (AD) and mild cognitive impairment (MCI), which includes MCIc (MCI converted to AD) and MCInc (MCI not converted to AD). In our previous study, we proposed an AD classification approach based on individual hierarchical networks constructed with 3D texture features of brain images. However, we only used edge features of the networks without node features of the networks. In this paper, we propose a framework of the combination of multiple kernels to combine edge features and node features for AD classification. An evaluation of the proposed approach has been conducted with MRI images of 710 subjects (230 health controls (HC), 280 MCI (including 120 MCIc and 160 MCInc), and 200 AD) from the Alzheimer's disease neuroimaging initiative database by using ten-fold cross validation. Experimental results show that the proposed method is not only superior to the existing AD classification methods, but also efficient and promising for clinical applications for the diagnosis of AD via MRI images. Furthermore, the results also indicate that 3D texture could detect the subtle texture differences between tissues in AD, MCI, and HC, and texture features of MRI images might be related to the severity of AD cognitive impairment. These results suggest that 3D texture is a useful aid in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".