@misc{10481/57351,
year = {2019},
month = {7},
url = {http://hdl.handle.net/10481/57351},
abstract = {Computer aided diagnosis systems based on brain imaging are an important tool to
assist in the diagnosis of Parkinson’s disease, whose ultimate goal is the detection
by automatic recognizing of patterns that characterize the disease. In recent times
Convolutional Neural Networks (CNN) have proved to be amazingly useful for that
task. The drawback, however, is that 3D brain images contain a huge amount of
information that leads to complex CNN architectures. When these architectures become
too complex, classification performances often degrades because the limitations of the
training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as
a way to reduce such amount of data while keeping the most relevant information.
These isosurfaces are then used to implement a classification system which uses two
of the most well-known CNN architectures, LeNet and AlexNet, 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.},
organization = {MINECO/FEDER under
TEC2015-64718-R, PSI2015-65848-R, PGC2018-098813-B-C32,
and RTI2018-098913-B-100 projects.},
publisher = {Frontiers in Neuroinformatics},
keywords = {Deep learning},
keywords = {isosurfaces},
keywords = {Parkinson´s disease},
keywords = {Convolutional neural networks},
keywords = {computer-aided diagnosis},
title = {Parkinson’s Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks},
doi = {10.3389/fninf.2019.00048},
author = {Ortiz, Andrés and Munilla, Jorge and Martínez Íbañez, Manuel and Gorriz Sáez, Juan Manuel and Ramírez Pérez De Inestrosa, Javier and Salas-González, Diego},
}