Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis
Bibliographic record
Abstract
Alzheimer's disease, a debilitating neurological disorder, precipitates irreversible cognitive decline and memory loss, predominantly affecting individuals aged 65 years and above.The need for an automated system capable of accurately diagnosing and stratifying Alzheimer's disease into distinct stages is paramount for early intervention and management.However, existing deep learning methodologies are often hampered by protracted training times.In this study, a time-efficient approach incorporating a two-phase transfer learning technique is proposed to surmount this challenge.This method is particularly efficacious in the analysis of Magnetic Resonance Imaging (MRI) data for the identification of Alzheimer's disease.The proposed detection system employs two-phase transfer learning, augmented with fine-tuning for multi-class classification of brain MRI scans.This allows for the categorization of images into four distinct classes: Mild Dementia (MD), Moderate Dementia (MOD), Non-Dementia (ND), and Very Mild Dementia (VMD).The classification of Alzheimer's disease was conducted using various pre-trained deep learning models, including ResNet50V2, InceptionResNetV2, Xception, DenseNet121, VGG16, and MobileNetV2.Among the models tested, ResNet50V2 demonstrated superior performance, achieving a training classification accuracy of 99.35% and a testing accuracy of 99.25%.The results underscore the potential of the proposed method in delivering more accurate classifications than those obtained from extant models, thereby contributing to the early detection and stratification of Alzheimer's disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
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".