An Intelligent Computing Based Approach for Parkinson Disease Detection

Ashis, Ranjan; Swetapadma, Aleena

doi:10.1109/icaecc.2018.8479490

Cited by 4 publications

(4 citation statements)

References 3 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%

“…In [ 38 , 83 ], we found that long short-term memory (LSTM) achieved outstanding results, indicating the best option to deal with EEG data. On the other hand, seven studies [ 3 , 19 , 25 , 27 , 40 , 69 , 101 , 102 ] focused on the classification of handwriting image to identify PD in the early stage, and we found that outstanding results were achieved in ANN + SVM in [ 3 ], dual-path RNN (DPRNN) in [ 40 ], and CNN + Optimum-Path Forest (OPF) in [ 102 ], respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Any minor damage to any of its parts will severely affect other organs—one of its adverse effects is Parkinson’s disease (PD) [ 1 ]. “PD is a chronic and progressive neurodegenerative disease” [ 2 ], and it occurs mainly in people over 50 years old [ 3 ]. Its symptoms start slowly and increase over time.…”

Section: Introductionmentioning

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%

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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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“…Ranjan and Swetapadma [3] implemented Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) for the detection of Parkinson's Disease. After carrying out a comparative study among these three proposed methodology it was found to have an accuracy of around 100% for the testing dataset.…”

Section: Related Workmentioning

confidence: 99%