Classifying Alzheimer's Disease Using Hybrid Model: Xception and Machine Learning
Bibliographic record
Abstract
This research introduces a novel approach to Alzheimer's disease detection by combining Xception's efficiency with machine learning classifiers, notably XGBoost.The hybrid model strategically uses Xception for feature extraction and integrates machine learning algorithms to enhance early detection accuracy, leveraging depthwise separable convolution for reduced computational complexity.Addressing imbalanced data, the study incorporates SMOTE, showcasing the hybrid model's effectiveness.Before SMOTE, the model achieved 72.89% accuracy and a 74.35% F1 score, outperforming the non-hybrid Xception model.Post-SMOTE, accuracy increases to 86.75%, and the F1 score to 86.84%, demonstrating substantial improvement without excessive computational demands.In comparison, the non-hybrid Xception model exhibits 78.71% accuracy and a 78.27%F1 score after SMOTE, emphasizing the pronounced enhancement achieved by the hybrid model.The Kaggle-derived dataset, totaling 6400 images, undergoes meticulous preprocessing, acknowledging dataset-specific constraints on generalizability.Emphasizing the importance of addressing data imbalance for robust classification, the hybrid model offers a promising solution for accurate and efficient Alzheimer's disease detection.This study contributes valuable insights to the field, showcasing the potential of innovative hybrid models to address complex healthcare challenges while balancing accuracy and computational efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".