Factors associated with delay in diagnosis of oral cancers.

Background: Oral cancer is one of the ten most common malignancies in the world and approximately 90 % of cases are OSCC. Despite the progress in available treatment modalities, the mortality of patients with OSCC has remained steadily high during the last 20 years. Survival data is strongly influenced by the timing of diagnosis: with more than 50 % of patients being diagnosed at an advanced stage, and their 5-year survival rate being less than 50 %.

GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes.