2019
DOI: 10.1109/access.2019.2931744
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Data Driven Intelligent Diagnostics for Parkinson’s Disease

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Cited by 29 publications
(20 citation statements)
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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%
See 1 more Smart Citation
“…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%
“…Combining down-sampling ways with up-sampling ways present a U-shape. Inspired by the deformable convolution neural network which was published by Dai in 2019 [26], this work replaces typical convolution kernel with deformable convolution in the whole network. The improved U-Net architecture is shown in Figure 14.…”
Section: Improved Structure Of Deformable U-net (Deu-net)mentioning
confidence: 99%
“…Hence, the segmentation of the lumen and carotid artery boundaries in bifurcation regions is expected to be considered in future researches. Although Deep Learning approaches have been a topic of research highly discussed and used in many studies related to image segmentation and classification tasks ( [47,48,49,50,51,52,53]), the proposed method presented here is aimed for the identification of structures in medical images by means of well-known image processing and analysis algorithms. However, we also intend to consider Deep Learning techniques for the segmentation of the structures of the carotid arteries in future works.…”
Section: Limitationsmentioning
confidence: 99%