ArticleDiagnostics (Basel, Switzerland)2022
An Ensemble of CNN Models for Parkinson's Disease Detection Using DaTscan Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Hybrid deep learning novel framework for classification of parkinson's disease.Scientific reports · 2026Article
- Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson's disease.Frontiers in digital health · 2026Article
- Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis.Scientific reports · 2025Article
- Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.Annals of nuclear medicine · 2025Article
- Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.BMC medical imaging · 2024Article
- A review of emergent intelligent systems for the detection of Parkinson's disease.Biomedical engineering letters · 2023Review
- An Improved Method for Diagnosis of Parkinson's Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm.Research square · 2023Article
- Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment.Diagnostics (Basel, Switzerland) · 2023Article
- An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Paroxysmal Slow-Wave Events Are Uncommon in Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's Disease (PD) is a progressive central nervous system disorder that is caused due to the neural degeneration mainly in the substantia nigra in the brain. It is responsible for the decline of various motor functions due to the loss of dopamine-producing neurons. Tremors in hands is usually the initial symptom, followed by rigidity, bradykinesia, postural instability, and impaired balance. Proper diagnosis and preventive treatment can help patients improve their quality of life. We have proposed an ensemble of Deep Learning (DL) models to predict Parkinson's using DaTscan images. Initially, we have used four DL models, namely, VGG16, ResNet50, Inception-V3, and Xception, to classify Parkinson's disease. In the next stage, we have applied a Fuzzy Fusion logic-based ensemble approach to enhance the overall result of the classification model. The proposed model is assessed on a publicly available database provided by the Parkinson's Progression Markers Initiative (PPMI). The achieved recognition accuracy, Precision, Sensitivity, Specificity, F1-score from the proposed model are 98.45%, 98.84%, 98.84%, 97.67%, and 98.84%, respectively which are higher than the individual model. We have also developed a Graphical User Interface (GUI)-based software tool for public use that instantly detects all classes using Magnetic Resonance Imaging (MRI) with reasonable accuracy. The proposed method offers better performance compared to other state-of-the-art methods in detecting PD. The developed GUI-based software tool can play a significant role in detecting the disease in real-time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.