Authors
Jianjun Ren, Xueping Jing, Jing Wang, Xue Ren, Yang Xu, Qiuyun Yang, Lanzhi Ma, Yi Sun, Wei Xu, Ning Yang, Jian Zou, Yongbo Zheng, Min Chen, Weigang Gan, Ting Xiang, Junnan An, Ruiqing Liu, Cao Lv, Ken Lin, Xianfeng Zheng, Fan Lou, Yufang Rao, Hui Yang, Kai Liu, Geoffrey Liu, Tao Lu, Xiujuan Zheng, Yu Zhao
Publication date
2020/11
Journal
The Laryngoscope
Volume
130
Issue
11
Pages
E686-E693
Publisher
John Wiley & Sons, Inc.
Description
Objectives/Hypothesis
To develop a deep‐learning–based computer‐aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician‐based accuracy of diagnostic assessments of laryngoscopy findings.
Study Design
Retrospective study.
Methods
A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)‐based classifier. A comparison between the proposed CNN‐based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted.
Results
In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2 …
Scholar articles
J Ren, X Jing, J Wang, X Ren, Y Xu, Q Yang, L Ma… - The Laryngoscope, 2020