Combining deep features and hand-crafted features for abnormality detection in WCE images

Abstract

In this paper, a computer-aided method is proposed for abnormality detection in Wireless Capsule Endoscopy (WCE) video frames. Common abnormalities in WCE images include ulcers, bleeding, Angiodysplasia, Lymphoid Hyperplasia, and polyp. In this paper, deep features and Hand-crafted features are combined to detect these abnormalities in WCE images. There are not sufficient images available to train deep structures, therefore the ResNet50 pre-trained model is used to extract deep features. Hand-crafted features are associated with color, shape, and texture. We used a novel idea to reveal unexpected color changes in the background due to existing lesions as a color feature set. Histogram of gradient (HOG) and local binary pattern (LBP) were used respectively for shape and texture features. They are extracted from the region of interest (ROI), i.e. suspicious region. The expectation Maximization (EM) algorithm is used to extract more distinct areas in the background as ROI. The expectation Maximization (EM) algorithm is configured in a way that can extract areas with a distinct texture and color as ROI. The EM algorithm is also initialized with a new fast method which leads to an increase in the accuracy of the method. A large number of features are created by the method, so the minimum redundancy maximum relevance approach is used to select a subset of more effective features. These selected features are then fed to a Support Vector Machine for classification. The results show that the proposed approach can detect mentioned abnormalities in WCE frames with the accuracy of 97.82%