Parkinson’s Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks
Metadatos
Afficher la notice complèteAuteur
Ortiz, Andrés; Munilla, Jorge; Martínez Íbañez, Manuel; Gorriz Sáez, Juan Manuel; Ramírez Pérez De Inestrosa, Javier; Salas-González, DiegoEditorial
Frontiers in Neuroinformatics
Materia
Deep learning isosurfaces Parkinson´s disease Convolutional neural networks computer-aided diagnosis
Date
2019-07-02Referencia bibliográfica
Ortiz A, Munilla J, Martínez-Ibañez M, Górriz JM, Ramírez J and Salas-Gonzalez D (2019) Parkinson’s Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks. Front. Neuroinform. 13:48
Patrocinador
MINECO/FEDER under TEC2015-64718-R, PSI2015-65848-R, PGC2018-098813-B-C32, and RTI2018-098913-B-100 projects.Résumé
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.