Laryngeal tumor grading is a challenging task for computer-aided clinical diagnosis (CACD),
mainly because the nuclei in histopathological images have large differences in shape and …

Abstract Background Oral Squamous Cell Carcinoma (OSCC) is one of the most severe
cancers in the world, and its early detection is crucial for saving patients. There is an …

Introduction Artificial intelligence models and networks can learn and process dense
information in a short time, leading to an efficient, objective, and accurate clinical and …

Squamous cell carcinoma (SCC) is the most common and malignant laryngeal cancer. An
early-stage diagnosis is of crucial importance to lower patient mortality and preserve both …

The classification of benign and malignant parotid tumors is very crucial for the selection of
surgical methods and their prognoses. The wide application of deep learning technology in …

METHODS: A total of 4,002 images from 1,078 patients were used to train and cross-validate
the DCNN model for diagnosing early ESCC. The performance of the model was further …

The study aims to identify histological classifiers from histopathological images of oral
squamous cell carcinoma using convolutional neural network (CNN) deep learning models …

Purpose Accurate lymph node (LN) malignancy classification is essential for treatment target
identification in head and neck cancer (HNC) radiation therapy. Given the constraints …

Background: Early diagnosis of laryngeal lesions is necessary to begin treatment of patients
as soon as possible to preserve optimal organ functions. Imaging examinations are often …

Objectives To develop deep learning–assisted diagnosis models based on CT images to
facilitate radiologists in differentiating benign and malignant parotid tumors. Methods Data …