A deep convolutional neural network-based method for laryngeal squamous cell carcinoma diagnosis.

ABSTRACT:

Background

Laryngeal squamous cell carcinoma (LSCC) is one of the most common tumors of the respiratory tract. Currently, the diagnosis of LSCC is mainly based on a laryngoscopy analysis and pathological findings. Deep-learning algorithms have been shown to provide accurate clinical diagnoses.

Methods

We developed a deep convolutional neural network (CNN) model, and evaluated its application to narrow-band imaging (NBI) endoscopy and pathological diagnoses of LSCC at several hospitals. A total of 4,591 patients' laryngeal NBI scans (1,927 benign and 2,664 LSCC) were used to test and validate the model. Additionally, 3,458 pathological images (752 benign and 2,706 LSCC) of 1,228 patients' hematoxylin and eosin staining slides (318 benign and 910 LSCC) were used for the pathol

SUBMITTER: He Y 

PROVIDER: S-EPMC8756237 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature