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...

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Vydáno v:Multimedia tools and applications Ročník 79; číslo 21-22; s. 15467 - 15479
Hlavní autoři: Sivaranjini, S., Sujatha, C. M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2020
Springer Nature B.V
Témata:
Artificial neural networks
Brain
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Diagnosis
Disease control
Dopamine
Image classification
Magnetic resonance imaging
Medical imaging
Motors
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
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Shrnutí: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.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-7469-8