Classification of esophageal cancer CT images using deep learning

Abstract

Esophageal cancer has a 10-year survival rate of about 20%, which is a cancer with a high mortality rate along with pancreatic cancer. It is also known that diagnosis of cancer by CT images is difficult to distinguish between peristaltic movement and cancer stenosis in the gastrointestinal tract such as the esophagus. Therefore, in this study, by performing image recognition learning a CT image of a patient diagnosed as esophageal cancer in the past with a convolution neural network (CNN) and a recurrent neural network (LSTM), it is aimed to construct a system for discriminating the presence or absence of cancer from a new CT image. As a result, we succeeded in a classification model of esophageal cancer using CNN and LSTM, and it to classify with more than 80% accuracy.