Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network.

ABSTRACT:

Background

Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer. This study was performed to develop an automatic and accurate imaging processing technique system, allowing this system to read computed tomography (CT) images correctly and make diagnosis of pancreatic cancer faster.

Methods

The establishment of the artificial intelligence (AI) system for pancreatic cancer diagnosis based on sequential contrast-enhanced CT images were composed of two processes: training and verification. During training process, our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set. Additionally, we used VGG16, which was pre-trained in ImageNet and contained 13 convolutional laye

SUBMITTER: Liu SL 

PROVIDER: S-EPMC6940082 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature