Automated detection of colorectal tumors based on artificial intelligence.

ABSTRACT:

Background

This study developed a diagnostic tool to automatically detect normal, unclear and tumor images from colonoscopy videos using artificial intelligence.

Methods

For the creation of training and validation sets, 47,555 images in the jpg format were extracted from colonoscopy videos for 24 patients in Korea University Anam Hospital. A gastroenterologist with the clinical experience of 15 years divided the 47,555 images into three classes of Normal (25,895), Unclear (2038) and Tumor (19,622). A single shot detector, a deep learning framework designed for object detection, was trained using the 47,255 images and validated with two sets of 300 images-each validation set included 150 images (50 normal, 50 unclear and 50 tumor cases). Half of the 47,255 images were used fo

SUBMITTER: Lee KS 

PROVIDER: S-EPMC7849081 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature