A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study.

ABSTRACT:

Background

The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers' clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images.

Methods

We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between A

SUBMITTER: Fu Q 

PROVIDER: S-EPMC7599313 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature