ArticleFrontiers in neuroinformatics2019
Parkinson's Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 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
- Early Detection of Parkinson's Disease Using Automatic Classification of Single-photon Emission Computed Tomography Images.Healthcare informatics research · 2026Article
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- 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
- An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.BMC medical imaging · 2024Article
- White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.Quantitative imaging in medicine and surgery · 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
- Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.PeerJ. Computer science · 2023Article
- An Ensemble of CNN Models for Parkinson's Disease Detection Using DaTscan Images.Diagnostics (Basel, Switzerland) · 2022Article
- Aberrant Structure MRI in Parkinson's Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis.Journal of personalized medicine · 2022Article
- Soft Attention Based DenseNet Model for Parkinson's Disease Classification Using SPECT Images.Frontiers in aging neuroscience · 2022Article
- Self-normalized Classification of Parkinson's Disease DaTscan Images.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).Sensors (Basel, Switzerland) · 2021Review
- The role of the deep convolutional neural network as an aid to interpreting brain [European radiology · 2021Article
- The Role of Neural Network for the Detection of Parkinson's Disease: A Scoping Review.Healthcare (Basel, Switzerland) · 2021Article
- Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review.Annals of translational medicine · 2021Review
- Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis.Brain and behavior · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson's disease, whose ultimate goal is the detection by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contain a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades because the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to implement a classification system which uses two of the most well-known CNN architectures, LeNet and AlexNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.
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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.