Deep Learning Models for Early Detection of Alzheimer’s Disease Using Neuroimaging Data
Bibliographic record
Abstract
Early identification is essential for successful intervention in Alzheimer's disease, a progressive neurodegenerative disease that is a major contributor to cognitive loss in older persons. Alzheimer's disease is difficult to detect in its early stages using conventional diagnostic techniques like neuroimaging and cognitive tests. This study investigates the use of deep learning models specifically, Convolutional Neural Networks, or CNNs to neuroimaging data to diagnose Alzheimer's disease early and the prognostic ability of Alzheimer-signature MRI biomarkers in detecting the change in cognitively normal persons into those with Alzheimer's disease (AD) dementia. Based on secondary data taken from the literature, this study assesses the performance of many deep learning architectures, such as Dense Net models, Graph Convolutional Networks (GCNs), and 3D CNNs as well as biomarkers. According to our research, CNN-based models hold great potential for precise Alzheimer's disease identification, particularly when they use three-dimensional imaging data. CNNs are the most commonly used architecture, according to a comparative study of 22 reviewed research; other models, such as GCNs and fine-tuned VGG19, exhibit noteworthy performance. The clinical applicability of such deep learning techniques and their capacity to improve patient outcomes and diagnostic precision in Alzheimer's care are also covered in this research. The study ends with suggestions for additional research, with an emphasis on addressing dataset variability limits and optimizing the model.
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 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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".