Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

ABSTRACT: High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. Therefore, we created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer's Disease Neuroimaging Initiative dataset. Spatial normalization, count normalization, and skull stripping preprocessing were performed on the DAUH and external datasets. However, smoothing was only performed on the external dataset. Three types of models were used, depend

SUBMITTER: Lee SY 

PROVIDER: S-EPMC8528306 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature