Convolutional neural network-based classification of cervical intraepithelial neoplasias using colposcopic image segmentation for acetowhite epithelium.

ABSTRACT: Colposcopy is a test performed to detect precancerous lesions of cervical cancer. Since cervical cancer progresses slowly, finding and treating precancerous lesions helps prevent cervical cancer. In particular, it is clinically important to detect high-grade squamous intraepithelial lesions (HSIL) that require surgical treatment among precancerous lesions of cervix. There have been several studies using convolutional neural network (CNN) for classifying colposcopic images. However, no studies have been reported on using the segmentation technique to detect HSIL. In present study, we aimed to examine whether the accuracy of a CNN model in detecting HSIL from colposcopic images can be improved when segmentation information for acetowhite epithelium is added. Without segmentation information,

SUBMITTER: Kim J 

PROVIDER: S-EPMC9568549 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature