Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images

Gastrointestinal tract (GIT) infections such as ulcers, bleeding, polyps, Crohn’s disease and cancer are quite familiar today worldwide. Wireless capsule endoscopy (WCE) is an efficient means of investigation of GIT diseases. However, still several challenges exist in this domain, such as lesion shape, colour, texture, size and irregularity. To deal with these problems, several computer-based methods are introduced in computer vision domain but they used only hand-crafted features which produced wrong predictions several times. In this research, a new technique is applied based on the fusion of deep convolutional (CNN) and geometric features. Initially, disease regions are extracted from given WCE images using a new approach named contrast-enhanced colour features. Geometric features are extracted from segmented disease region. Thereafter, unique VGG16 and VGG19 deep CNN features fusion are performed based on Euclidean Fisher Vector. The unique features are fused with geometric features which are later fed to conditional entropy approach for best features selection. The selected features are finally classified by K-Nearest Neighbour. A privately collected database which consists of 5500 WCE images is utilised for the evaluation of the proposed method and achieved best classification accuracy of 99.42% and precision rate of 99.51%. The classification accuracy proves the authenticity of the proposed approach.