Research Area:  Machine Learning

Abstract:

Parkinson 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 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 disease subjects. In this work, an attempt has been made to classify the MR images of healthy control and Parkinson disease subjects using deep learning neural network. The Convolutional Neural Network architecture AlexNet is used to refine the diagnosis of Parkinson 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 disease and yield an objective and better patient group classification in the near future.

Keywords:  
Parkinson disease
MRI
Deep learning
Convolutional neural networks
AlexNet

Author(s) Name:  S. Sivaranjini, C. M. Sujatha

Publisher name:  Springer

DOI:  10.1007/s11042-019-7469-8

Volume Information:  79, pages 15467–15479

Paper Link:   https://link.springer.com/article/10.1007/s11042-019-7469-8