ArticleMolecules (Basel, Switzerland)2020
Classification of the Multiple Stages of Parkinson's Disease by a Deep Convolution Neural Network Based on
Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A quantum-classical dual-track deep learning network for explainable Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- Advanced optimization strategies for combining acoustic features and speech recognition error rates in multi-stage classification of Parkinson's disease severity.Biomedical engineering letters · 2025Article
- Enhancing early Parkinson's disease detection through multimodal deep learning and explainable AI: insights from the PPMI database.Scientific reports · 2024Article
- Radiomics incorporating deep features for predicting Parkinson's disease inEJNMMI physics · 2024Article
- Parkinson's Disease and Photobiomodulation: Potential for Treatment.Journal of personalized medicine · 2024Review
- Hybrid Machine Learning Framework for Multistage Parkinson's Disease Classification Using Acoustic Features of Sustained Korean Vowels.Bioengineering (Basel, Switzerland) · 2023Article
- Deletion of Tfam in Prx1-Cre expressing limb mesenchyme results in spontaneous bone fractures.Journal of bone and mineral metabolism · 2022Article
- Low-Parameter Small Convolutional Neural Network Applied to Functional Medical Imaging of Tc-99m Trodat-1 Brain Single-Photon Emission Computed Tomography for Parkinson's Disease.Journal of personalized medicine · 2021Article
- Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).Sensors (Basel, Switzerland) · 2021Review
- Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021Review
- Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint-Node Plots.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
10 authors.
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Abstract
Single photon emission computed tomography (SPECT) has been employed to detect Parkinson's disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models' performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).
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