XSH-ParK: XAI-based Parkinson Disease Diagnosis Framework For Smart Healthcare Using MRI Images
Bibliographic record
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease which is the second most common neurological disease. Early diagnosis of PD poses significant challenges as in earlier stages of PD, symptoms can’t be clinically recognized. This paper presents a framework called XSH-ParK with integrated deep learning (DL) models and XAI techniques to assist in early PD diagnosis using MRI scans. Pre-trained models VGG16, InceptionV3, ResNet50 and a custom CNN are used to analyze the NTUA dataset, which consists of MRI scans of 78 individuals. Through rigorous evaluation considering accuracy, precision, recall, and F1-score metrics, it is evident that the fine-tuned VGG16 model achieves the highest efficiency with an accuracy rate of 97.56% in the XSH-ParK framework. Additionally, LIME and integrated gradient are the XAI methods used on the top-performing VGG16 model to provide transparent and interpretable diagnostic insights, Enabling healthcare professionals to understand the reasons behind the models’ decisions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".