Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alzheimer is a type of dementia disease that is common in older ages. This disease is a progressive form of neurological disease that causes the destruction of brain cells. Since Alzheimer's is a progressive disease, various problems increase over time. For this reason, it is very important to diagnose the disease early and start the treatment process. In this study, it was tried to determine at which stage the disease is or whether it is Alzheimer using brain images. CNN architectures are used to diagnose the disease. In addition, a hybrid method we have developed has been proposed. With the architectures used, it is classified in 4 stages according to the disease progression level. In the proposed hybrid model, the Resnet50 method is used as the basis. The results are obtained separately by Alexnet, Resnet50, Densenet201, Vgg16, and the Hybrid method we developed. An accuracy of 90% has been achieved with the developed hybrid model. Consequently, when other scientific paper in the literature are investigated, it is finalized that the hybrid model developed to diagnose Alzheimer's disease has achieved the success achieved by other CNN architectures and even offers better results.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 it