Automatic Recognition of Laryngoscopic Images Using a Deep‐Learning Technique

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% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN‐based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P