Enhanced Parkinson’s Disease Diagnosis Through Convolutional Neural Network Models Applied to SPECT DaTSCAN Images
Bibliographic record
Abstract
Convolutional Neural Networks (CNNs) are highly regarded in Deep Learning (DL) and have shown promising results in medical image analysis, making them a leading model for Computer-Aided Diagnosis (CAD) systems. Their efficacy extends to the diagnosis of neurological disorders, including Parkinson’s Disease (PD), which is typically identified through Single Photon Emission Computed Tomography (SPECT) scans. However, relying solely on visual inspection of SPECT images during medical examinations can introduce inaccuracies due to subjective factors. We propose a CAD system for automatic PD diagnosis using pre-trained CNN models, Transfer Learning (TL) technique, and the Bilinear Pooling method to address this issue. The study employs several CNN architectures, specifically Efficient-Net B0, and Mobile-Net V2 models, and a custom CNN architecture. These pre-tained architectures were originally trained on ImageNet and adapted to the current task using the TL technique. These architectures are leveraged with a bilinear pooling form, resulting in three Bilinear CNN (BCNN) models. These models are applied to pre-processed SPECT image data of PD patients and Healthy Controls (HC), categorized into three distinct datasets. The proposed method is evaluated on a total of 2720 SPECT images (1360 PD and 1360 HC subjects) obtained from the Parkinson’s Progression Marker Initiative (PPMI) dataset. The findings show that the BCNN EfficientNet-B0-MobileNet-V2 model achieved the highest accuracy score of 99.14%, surpassing other developed CNN models and outperforming existing methods. In conclusion, the proposed CAD system provides an efficient diagnostic tool to assist physicians in making accurate PD diagnoses, independent of subjective factors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".