Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging

In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorithm combined with 3D brain magnetic resonance imaging (MRI) in diagnosis of cerebral palsy in children. 3D brain model was segmented based on CNN algor...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Rui Yang, Haoran Zuo, Shusheng Han, Xiaoping Zhang, Qian Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/5fd8f7624b4e4b1ea56f57ab7e42c22a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5fd8f7624b4e4b1ea56f57ab7e42c22a
record_format	dspace
spelling	oai:doaj.org-article:5fd8f7624b4e4b1ea56f57ab7e42c22a2021-11-22T01:11:12ZComputer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging2040-230910.1155/2021/1822776https://doaj.org/article/5fd8f7624b4e4b1ea56f57ab7e42c22a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1822776https://doaj.org/toc/2040-2309In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorithm combined with 3D brain magnetic resonance imaging (MRI) in diagnosis of cerebral palsy in children. 3D brain model was segmented based on CNN algorithm to obtain the segmented MRI images of brain tissue, and the validity was verified. Then, 70 children with cerebral palsy were rolled into the observation group (n = 35), which received MRI for diagnosis after segmentation of brain tissue, and control group (n = 35), which were diagnosed by computed tomography (CT). The diagnosis results of the two groups were compared. The validity experiment verified that the image segmentation method based on CNN algorithm can obtain effective style graphics. In clinical trials, the diagnostic accuracy of 88.6% in the observation group was evidently superior to that of 80% in the control group (P<0.05). In the observation group, one patient was diagnosed as normal, four patients had white matter lesions, 17 patients had corpus callosum lesions, and five patients had basal ganglia softening foci. In the control group, two patients were diagnosed as normal, two patients had white matter lesions, 19 patients had corpus callosum lesions, and four patients had basal ganglia softening foci. No notable difference was found between the two groups (P>0.05). According to the research results, in the diagnosis of cerebral palsy in children, the image segmentation of brain 3D model based on CNN to obtain the MRI image of segmented brain tissue can effectively improve the detection accuracy. Moreover, the specific symptoms can be diagnosed clearly. It can provide the corresponding diagnostic basis for clinical diagnosis and treatment and was worthy of clinical promotion.Rui YangHaoran ZuoShusheng HanXiaoping ZhangQian ZhangHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Rui Yang Haoran Zuo Shusheng Han Xiaoping Zhang Qian Zhang Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging
description	In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorithm combined with 3D brain magnetic resonance imaging (MRI) in diagnosis of cerebral palsy in children. 3D brain model was segmented based on CNN algorithm to obtain the segmented MRI images of brain tissue, and the validity was verified. Then, 70 children with cerebral palsy were rolled into the observation group (n = 35), which received MRI for diagnosis after segmentation of brain tissue, and control group (n = 35), which were diagnosed by computed tomography (CT). The diagnosis results of the two groups were compared. The validity experiment verified that the image segmentation method based on CNN algorithm can obtain effective style graphics. In clinical trials, the diagnostic accuracy of 88.6% in the observation group was evidently superior to that of 80% in the control group (P<0.05). In the observation group, one patient was diagnosed as normal, four patients had white matter lesions, 17 patients had corpus callosum lesions, and five patients had basal ganglia softening foci. In the control group, two patients were diagnosed as normal, two patients had white matter lesions, 19 patients had corpus callosum lesions, and four patients had basal ganglia softening foci. No notable difference was found between the two groups (P>0.05). According to the research results, in the diagnosis of cerebral palsy in children, the image segmentation of brain 3D model based on CNN to obtain the MRI image of segmented brain tissue can effectively improve the detection accuracy. Moreover, the specific symptoms can be diagnosed clearly. It can provide the corresponding diagnostic basis for clinical diagnosis and treatment and was worthy of clinical promotion.
format	article
author	Rui Yang Haoran Zuo Shusheng Han Xiaoping Zhang Qian Zhang
author_facet	Rui Yang Haoran Zuo Shusheng Han Xiaoping Zhang Qian Zhang
author_sort	Rui Yang
title	Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging
title_short	Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging
title_full	Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging
title_fullStr	Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging
title_full_unstemmed	Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging
title_sort	computer-aided diagnosis of children with cerebral palsy under deep learning convolutional neural network image segmentation model combined with three-dimensional cranial magnetic resonance imaging
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/5fd8f7624b4e4b1ea56f57ab7e42c22a
work_keys_str_mv	AT ruiyang computeraideddiagnosisofchildrenwithcerebralpalsyunderdeeplearningconvolutionalneuralnetworkimagesegmentationmodelcombinedwiththreedimensionalcranialmagneticresonanceimaging AT haoranzuo computeraideddiagnosisofchildrenwithcerebralpalsyunderdeeplearningconvolutionalneuralnetworkimagesegmentationmodelcombinedwiththreedimensionalcranialmagneticresonanceimaging AT shushenghan computeraideddiagnosisofchildrenwithcerebralpalsyunderdeeplearningconvolutionalneuralnetworkimagesegmentationmodelcombinedwiththreedimensionalcranialmagneticresonanceimaging AT xiaopingzhang computeraideddiagnosisofchildrenwithcerebralpalsyunderdeeplearningconvolutionalneuralnetworkimagesegmentationmodelcombinedwiththreedimensionalcranialmagneticresonanceimaging AT qianzhang computeraideddiagnosisofchildrenwithcerebralpalsyunderdeeplearningconvolutionalneuralnetworkimagesegmentationmodelcombinedwiththreedimensionalcranialmagneticresonanceimaging
_version_	1718418283267358720

Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging

Ejemplares similares