Automatic classification of informative laryngoscopic images using deep learning.

ABSTRACT:

Objective

This study aims to develop and validate a convolutional neural network (CNN)-based algorithm for automatic selection of informative frames in flexible laryngoscopic videos. The classifier has the potential to aid in the development of computer-aided diagnosis systems and reduce data processing time for clinician-computer scientist teams.

Methods

A dataset of 22,132 laryngoscopic frames was extracted from 137 flexible laryngostroboscopic videos from 115 patients. 55 videos were from healthy patients with no laryngeal pathology and 82 videos were from patients with vocal fold polyps. The extracted frames were manually labeled as informative or uninformative by two independent reviewers based on vocal fold visibility, lighting, focus, and camera distance, resulting in

SUBMITTER: Yao P 

PROVIDER: S-EPMC9008155 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature