Deep learning model for tongue cancer diagnosis using endoscopic images

Jaesung Heo, June Hyuck Lim, Hye Ran Lee, Jeon Yeob Jang, Yoo Seob Shin, Dahee Kim, Jae Yol Lim, Young Min Park, Yoon Woo Koh, Soon Hyun Ahn, Eun Jae Chung, Doh Young Lee, Jungirl Seok, Chul Ho Kim

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, we developed a deep learning model to identify patients with tongue cancer based on a validated dataset comprising oral endoscopic images. We retrospectively constructed a dataset of 12,400 verified endoscopic images from five university hospitals in South Korea, collected between 2010 and 2020 with the participation of otolaryngologists. To calculate the probability of malignancy using various convolutional neural network (CNN) architectures, several deep learning models were developed. Of the 12,400 total images, 5576 images related to the tongue were extracted. The CNN models showed a mean area under the receiver operating characteristic curve (AUROC) of 0.845 and a mean area under the precision-recall curve (AUPRC) of 0.892. The results indicate that the best model was DenseNet169 (AUROC 0.895 and AUPRC 0.918). The deep learning model, general physicians, and oncology specialists had sensitivities of 81.1%, 77.3%, and 91.7%; specificities of 86.8%, 75.0%, and 90.9%; and accuracies of 84.7%, 75.9%, and 91.2%, respectively. Meanwhile, fair agreement between the oncologist and the developed model was shown for cancer diagnosis (kappa value = 0.685). The deep learning model developed based on the verified endoscopic image dataset showed acceptable performance in tongue cancer diagnosis.

Original languageEnglish
Article number6281
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

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