ParkinNet: a Novel Approach to Classifying Alzheimer’s and Parkinson’s Diseases Using Brain Structural MRI
Bibliographic record
Abstract
Both Parkinson’s disease (PD) and Alzheimer’s disease (AD) are forms of neurodegeneration, which are linked to the same biochemical alterations in the brain. The mixed pathology of these diseases may cause diagnostic dilemmas, which may lead to misdiagnosis. Because of this, classification of AD and PD is essential to reduce extra healthcare costs and the patients’ stress. However, the classification of AD and PD can be challenging because of the overlapping symptoms and risk factors. Therefore, the purpose of this study is to develop a model named ParkinNet to classify AD and PD. The current study used Global Average Pooling and Adam optimiser with a batch size of 64. For evaluation, seven deep learning algorithms are used, including MobileNetV2, EfficientNetB2, InceptionResNetv2, VGG16, VGG19, InceptionV3 and ResNet50, along with the proposed ParkinNet model. The proposed ParkinNet model outperforms the other existing models examined in this study and yields an accuracy of 98.54 %. The precise classification of these diseases may contribute to the diagnosis process of AD and PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".