Deep learning based diagnosis of Parkinson’s disease using convolutional neural network

Parkinson’s disease is the second most common degenerative disease caused by loss of dopamine producing neurons. The substantia nigra region is deprived of its neuronal functions causing striatal dopamine deficiency which remains as hallmark in Parkinson’s disease. Clinical diagnosis reveals a range of motor to non motor symptoms in these patients. Magnetic Resonance (MR) Imaging is able to capture the structural changes in the brain due to dopamine deficiency in Parkinson’s disease subjects. In this work, an attempt has been made to classify the MR images of healthy control and Parkinson’s disease subjects using deep learning neural network. The Convolutional Neural Network architecture AlexNet is used to refine the diagnosis of Parkinson’s disease. The MR images are trained by the transfer learned network and tested to give the accuracy measures. An accuracy of 88.9% is achieved with the proposed system. Deep learning models are able to help the clinicians in the diagnosis of Parkinson’s disease and yield an objective and better patient group classification in the near future. … celý popis

Podrobná bibliografie

Publikováno v

Multimedia tools and applications Ročník 79; číslo 21-22; s. 15467 - 15479

Hlavní autoři

Sivaranjini, S., Sujatha, C. M.

Typ dokumentu

Journal Article

Jazyk

English

Vydáno

New York Springer US 01. 06. 2020
Springer Nature B.V

Témata

AlexNet

Artificial neural networks

Brain

Computer Communication Networks

Computer Science

Convolutional neural networks

Data Structures and Information Theory

Deep learning

Diagnosis

Disease control

Dopamine

Image classification

Magnetic resonance imaging

Medical imaging

Motors

MRI

Multimedia Information Systems

Neural networks

Parkinson's disease

Signs and symptoms

Special Purpose and Application-Based Systems

Deep learning

AlexNet

Convolutional neural networks

MRI

Parkinson’s disease

ISSN

1380-7501
1573-7721

DOI

10.1007/s11042-019-7469-8