Parkinson's Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks.

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 clas

SUBMITTER: Ortiz A 

PROVIDER: S-EPMC6614282 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature