Parkinson’s Disease Detection from Spiral and Wave Drawings using Convolutional Neural Networks

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DC Field	Value	Language
dc.contributor.author	Charif, Fella	-
dc.contributor.author	Khelifa, Brahim	-
dc.contributor.author	Boukhris, Dia Errahmane	-
dc.date.accessioned	2024-10-07T09:31:31Z	-
dc.date.available	2024-10-07T09:31:31Z	-
dc.date.issued	2024	-
dc.identifier.citation	FACULTY OF NEW TECHNOLOGIES OF INFORMATION AND COMMUNICATION	en_US
dc.identifier.uri	https://dspace.univ-ouargla.dz/jspui/handle/123456789/37132	-
dc.description	Automatic and Systems	en_US
dc.description.abstract	Detecting parkinson's disease (PD) has become increasingly important in the medical field. Deep learning (DL), particularly Convolutional neural networks (CNNs), has shown great promise and has been extensively applied in various domains, including healthcare. this study introduces a detection system that leverages deep learning for a quick, accurate, and reliable PD diagnosis. Three pre-trained CNN models (ResNet50, DenseNet201, and AlexNet(are used for this system	en_US
dc.description.sponsorship	Department of Electronics and Telecommunication	en_US
dc.language.iso	en	en_US
dc.publisher	UNIVERSITY OF KASDI MERBAH OUARGLA	en_US
dc.subject	Deep Learning (DL)	en_US
dc.subject	Parkinson’s disease (PD)	en_US
dc.subject	Convolutional Neural Networks (CNNs)	en_US
dc.subject	ResNet50, DenseNet201	en_US
dc.subject	AlexNet	en_US
dc.title	Parkinson’s Disease Detection from Spiral and Wave Drawings using Convolutional Neural Networks	en_US
dc.type	Thesis	en_US
Appears in Collections:	Département d'Electronique et des Télécommunications - Master

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