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

Please use this identifier to cite or link to this item: https://dspace.univ-ouargla.dz/jspui/handle/123456789/37132

Title:	Parkinson’s Disease Detection from Spiral and Wave Drawings using Convolutional Neural Networks
Authors:	Charif, Fella Khelifa, Brahim Boukhris, Dia Errahmane
Keywords:	Deep Learning (DL) Parkinson’s disease (PD) Convolutional Neural Networks (CNNs) ResNet50, DenseNet201 AlexNet
Issue Date:	2024
Publisher:	UNIVERSITY OF KASDI MERBAH OUARGLA
Citation:	FACULTY OF NEW TECHNOLOGIES OF INFORMATION AND COMMUNICATION
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
Description:	Automatic and Systems
URI:	https://dspace.univ-ouargla.dz/jspui/handle/123456789/37132
Appears in Collections:	Département d'Electronique et des Télécommunications - Master

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