A fully automated approach involving neuroimaging and deep learning for Parkinson's disease detection and severity prediction.

ABSTRACT: Three-dimensional magnetic resonance imaging has been proved to detect and predict the severity of progressive neurodegenerative disorders such as Parkinson's disease. The application of pre-processing with neuroimaging methods plays a vital role in post-processing for these problems. The development of technology over the years has enabled the use of deep learning methods such as convolutional neural networks (CNN) on magnetic resonance imaging (MRI) . In this study, the detection of Parkinson's disease and the prediction of disease severity were studied with 2D and 3D CNN using T1-weighted MRIs that were pre-processed with FLIRT image registration and BET non-brain tissue scraper. For 2D CNN, the median slices of the MR images in the sagittal, coronal, and axial planes were used separate

SUBMITTER: Erdas CB 

PROVIDER: S-EPMC10403203 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature