Gastrointestinal polyp classification through empirical mode decomposition and neural features

Abstract

This study describes an automated detection of polyp type as it is very important to determine the existence of dysplasia—a stage leading to the development of gastrointestinal cancer. The polyp-type classification is performed by a multiclass support vector machine from feature-fusion of bi-dimensional empirical mode decomposition (BEMD) and convolutional neural network (CNN). An extensive experiment is performed using standard datasets by extracted features from the individual technique as well as a fusion of features from BEMD and CNN. The fusion technique confirms satisfactory performance compared to other techniques with an accuracy of 98.94%. Moreover, it shows potentiality in precisely classifying some challenging polyps even though these are somehow confusing for human experts.