A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI
Bibliographic record
Abstract
The early identification and treatment of Mild Cognitive Impairment (MCI) play a crucial role in managing the risk of Alzheimer’s disease (AD). However, current methods for categorizing progressive MCI and stable MCI based on brain MRI scans have proven insufficient due to the subtle nature of the features involved. This research aims to improve the effectiveness of MCI classification through the utilization of a Deep Learning (DL) network. The primary objective of this work is to improve the feature representation of brain MRI scans for more accurate classification. The proposed model is a hybrid MCI classification system that integrates three components: the Swin Transformer, the Dimension Centric Proximity Aware Attention Network (DCPAN), and the Age Deviation Factor (ADF). The proposed network achieves better classification results through a unique feature fusion approach that combines global, local, proximal features, and dimensional dependencies. It effectively combines fine-grained details with broader contextual information to extract discriminative features. Experimental results demonstrate the effectiveness of the proposed network, achieving an accuracy of 79.8%, precision of 76.6%, recall of 80.2%, and an F1-score of 78.4% when evaluated on the ADNI dataset.
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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 | 0.001 | 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) | 0.000 | 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".