Paper Info
Title
Isosurface Modelling Of Datscan Images For Parkinson Disease Diagnosis
Abstract
This paper proposes the computing of isosurfaces as a way to extract relevant features from 3D brain images. These isosurfaces are then used to implement a Computer aided diagnosis system to assist in the diagnosis of Parkinson's Disease (PD) which uses a most well-known Convolutional Neural Networks (CNN) architecture, LeNet, to classify DaTScan images with an average accuracy of 95.1% and AUC=97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.
Year
DOI
Venue
2019
10.1007/978-3-030-19591-5_37
UNDERSTANDING THE BRAIN FUNCTION AND EMOTIONS, PT I
Keywords
Field
DocType
Deep learning, Convolutional networks, Isosurfaces, Parkinson's Disease
Convolutional neural network,Computer science,Computer-aided diagnosis,Isosurface,Artificial intelligence,Deep learning,Machine learning,Computation
Conference
Volume
ISSN
Citations 
11486
0302-9743
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
M. Martínez-Ibañez100.34
Andrés Ortiz219525.64
Jorge Munilla331625.49
D. Salas-Gonzalez431226.61
J. M. Górriz557054.40
Javier Ramírez665668.23