Primary Categorizing and Masking Cerebral Small Vessel Disease Based on "Deep Learning System".

ABSTRACT:

Objective

To supply the attending doctor's diagnosis of the persisting of cerebral small vessel disease and speed up their work effectively, we developed a "deep learning system (DLS)" for cerebral small vessel disease predication. The reliability and the disease area segmentation accuracy, of the proposed DLS, was also investigated.

Methods

A deep learning model based on the convolutional neural network was designed and trained on 1,010 DWI b1000 images from 1010 patients diagnosed with segmentation of subcortical infarction, 359 T2^∗ images from 359 patients diagnosed with segmentation of cerebral microbleed, as well as 824 T1-weighted and T2-FLAIR images from 824 patients diagnosed with segmentation of lacune and WMH. Dicw accuracy, recall, and f1-score were cal

SUBMITTER: Duan Y 

PROVIDER: S-EPMC7261942 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature