Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using <scp>MRI</scp> images
Bibliographic record
Abstract
Abstract Alzheimer's disease (AD) is a neurodegenerative disease that causes memory loss and is considered the most common type of dementia. In many countries, AD is commonly affecting senior citizens having an aged more than 65 years. Machine learning‐based approaches have some limitations due to data pre‐processing issues. We propose a health care intelligent system based on a deep convolutional neural network (DCNN) in this research work. It classifies normal control (NC), mild cognitive impairment (MCI), and AD. The proposed model is employed on white matter (WM), and grey matter (GM) tissues with more cognitive decline features. In the experimental process, we used 375 Magnetic Resonance Image (MRI) subjects collected from Alzheimer's disease neuroimaging initiative (ADNI), including 130 NC people, 120 MCI patients, and 125 AD patients. We extract three major regions during pre‐processing, that is, WM, GM and cerebrospinal fluid (CSF). This study shows promising classification results for NC versus AD 97.94%, MCI versus AD 92.84%, and NC versus MCI 88.15% on GM images. Furthermore, our proposed model attained 95.97%, 90.82%, and 86.87% on the same three binary classes on WM tissue, respectively. When comparing existing studies in terms of accuracy and other evaluation parameters, we found that our proposed approach shows better results than those approaches based on the CNN method.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".