Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms.

ABSTRACT: The treatment plan of colorectal neoplasm differs based on histology. Although new endoscopic imaging systems have been developed, there are clear diagnostic thresholds and requirements in using them. To overcome these limitations, we trained convolutional neural networks (CNNs) with endoscopic images and developed a computer-aided diagnostic (CAD) system which predicts the pathologic histology of colorectal adenoma. We retrospectively collected colonoscopic images from two tertiary hospitals and labeled 3400 images into one of 4 classes according to the final histology: normal, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. We implemented a CAD system based on ensemble learning with three CNN models which transfer the knowledge learned from common digital photography image

SUBMITTER: Choi SJ 

PROVIDER: S-EPMC7935886 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature