Classification of Alzheimer’s Disease from MRI Data Using a Lightweight Deep Convolutional Model
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a progressive brain disorder affecting millions of people worldwide. An accurate diagnosis of AD plays a significant role in identifying the progression of the disease at its prodromal stage, i.e., mild cognitive impairment (MCI). In this paper, we propose a lightweight deep model to classify the patients into diagnostic groups, AD vs. normal control (NC) or progressive MCI (pMCI)vs. stable MCI (sMCI), with high accuracy, using MRI data. The proposed model uses separable and attention-based convolution operations. The separable convolution can reduce the complexity of the model by splitting a kernel into two separate kernels that do depth-wise and pointwise convolution operations, respectively. Moreover, integrating an attention-based convolution, which concatenates the convolutional and attentional feature maps, can capture the most relevant features for improved classification with fewer filters. From the experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) database, compared to the state-of-the-art methods, it is observed that the proposed method shows significant improvement in the classification performance in terms of accuracy, specificity, sensitivity, and AUC. In addition, the proposed method drastically reduces the number of parameters without affecting the performance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".