Alzheimer’s Disease Classification Using Wavelet-Based Image Features
Bibliographic record
Abstract
Alzheimer's disease (AD) is a big issue within a population of aged people.AD starts with cognitive decline initially and creates miserable conditions for patients with time.One of the best preventive measures to control AD is its early detection at the Mild Cognitive Impairment (MiCI) stage.The MiCI is a transition stage between normal ageing and AD.The MiCI stage refers to the noticeable decline in cognitive abilities of a patient, that is more pronounced than would be expected for his age but not severe enough to substantially affect his daily life.Early detection at MiCI stage allows for prompt intervention and medication, which can help manage symptoms more effectively.This paper proposed a new feature extraction technique namely, Wavelet-based Shifted Circular-Elliptical Local Descriptors (WSCELD) for early AD detection.The proposed WSCELD combines the Double-Density Dual-Tree Complex Wavelet Transform (DD-DTCWT) with the shifted elliptical and circular local binary patterns for extracting directional and structural features in terms of multiple micro and macro patterns.The histogram features are obtained from transform domain images using the proposed WSCELD and have been used for classification.Different variants of WSCELD viz.Mean WSCELD, Median WSCELD, Energy WSCELD and Variance WSCELD have been investigated and Energy WSCELD has been proposed.Experimental results show the Energy WSCELD as the best performer with classification accuracy, sensitivity, and specificity of 97.31.6%,97.11.2% and 97.21.1% for AD/Normal Controls (NoC) classification, 94.61.1%,96.11.2% and 93.11.1% for AD/MiCI classification and 93.81.4%,92.41.5% and 96.21.2% for MiCI/NoC classification respectively.The proposed approach is the automated approach for AD detection and is suitable for clinical implementation for early AD detection.
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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 | 0.000 |
| 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".