Deep Neural Networks with Broad Views for Parkinson's Disease Screening

Zhang, Xiaobo; Yang, Yan; Ning, Shangming; Wang, Heng

doi:10.1109/bibm47256.2019.8983000

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…Recently, with the developments of new techniques such as convolutional neural network [ 101 ] and transfer learning [ 63 ], deep learning gained significant advances in the computer vision tasks, e.g., ImageNet [ 77 ]. Therefore, most of the studies used different imaging data to diagnose PD, such as MRI ( n = 12) [ 41 , 47 , 54 , 56 , 58 , 66 , 72 , 78 , 82 , 86 , 90 , 95 ] and handwritten images ( n = 9) [ 3 , 19 , 25 , 30 , 69 , 75 , 101 , 102 ], as well as PET and CT imaging ( n = 6) [ 28 , 59 , 67 , 71 , 88 , 90 ] and DaTscan imaging ( n = 4) [ 54 , 76 , 99 , 103 ]. However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, for imaging dataset including MRI, PET CT, and DaTSCAN were mainly obtained from Parkinson Progression Markers Initiative (PPMI) to train classifier, as seen in [ 20 , 28 , 41 , 47 , 59 , 66 , 67 , 76 , 82 , 86 , 88 , 90 , 94 , 95 ]; hence, among all studies, CNN in [ 20 ] and FNN in [ 28 ] achieved an outstanding result for image classification.…”

Section: Discussionmentioning

confidence: 99%

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The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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“…MRI is usually considered by neurologists for the clinical diagnosis of neurological diseases. Zhang et al proposed a novel approach for screening de novo PD using ResNet (i.e., a deep CNN) with broad views using two-view MRI data (i.e., AXI and SAG) with an accuracy of 76.46% [ 30 ]. Ramirez et al introduced three fully convolutional Autoencoder models to detect de novo PD in Diffusion Tensor Imaging (DTI) MRI data with a best AUC of ROC of 77% [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease

Shaban

Amara

2022

PLoS ONE

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Parkinson’s disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease.

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“…The prodromal class with fewer AXI/SAG MRI data samples causes the problem of over-fitting or under-fitting in an application. Two ResNet networks are trained jointly on the two-view data to create more samples for the prodromal class in AXI and SAG [145].…”

Section: Data Augmentationmentioning

confidence: 99%

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

et al. 2022

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With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction process. Generative adversarial network (GAN)-synthesized images have many applications in this field besides augmentation, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. The existing literature was reviewed systematically to understand the role of GAN-synthesized dummy images in brain disease diagnosis. Web of Science and Scopus databases were extensively searched to find relevant studies from the last 6 years to write this systematic literature review (SLR). Predefined inclusion and exclusion criteria helped in filtering the search results. Data extraction is based on related research questions (RQ). This SLR identifies various loss functions used in the above applications and software to process brain MRIs. A comparative study of existing evaluation metrics for GAN-synthesized images helps choose the proper metric for an application. GAN-synthesized images will have a crucial role in the clinical sector in the coming years, and this paper gives a baseline for other researchers in the field.

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Deep Neural Networks with Broad Views for Parkinson's Disease Screening

Cited by 15 publications

References 17 publications

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

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