Classification of Capsule Endoscopy Images based on Feature Concatenation of Deep Neural Networks

Abstract

Wireless capsule endoscopy (WCE) is a noninvasive way of detecting abnormalities in digestive tract. These abnormalities need to be detected at the early stages before they turn malignant. The classification of these abnormalities has put many challenges due to the variations in lesion shape and color, lighting conditions, and other factors. Existing methods based on handcrafted features give less accuracy due to the limited capability of feature representation. This study proposes a new approach for classifying wireless capsule endoscopy images using feature concatenation of deep convolutional neural network models. The features of two pre-trained models are concatenated and tested using a newly created dataset. The dataset is created using images taken from the Kvasir capsule endoscopy and Red lesion endoscopy dataset which is publicly available. This system improves diagnostic efficiency and brings great assistance to the doctor.