Research Article An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features Mustain Billah, 1 Sajjad Waheed, 1 and Mohammad Motiur Rahman 2 1 Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh 2 Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Correspondence should be addressed to Mustain Billah; mustainbillahx@gmail.com Received 7 May 2017; Accepted 12 July 2017; Published 14 August 2017 Academic Editor: Tiange Zhuang Copyright © 2017 Mustain Billah et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Terefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in fnding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. Tis system captures the video streams from endoscopic video and, in the output, it shows the identifed polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specifcity of 98.52%. 1. Introduction Te most leading cause of death in the whole world is cancer. Again, gastrointestinal cancer is the most commonly occur- ring cancer which originates from gastrointestinal polyps. Actually, gastrointestinal polyps are the abnormal growth of tissue on gastric and colonic mucosa. Tis growth is a slow process and in majority of the cases, before reaching a large size, they do not produce symptoms. However, cancer is preventable and curable, if polyps could be detected early. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. In typical video endoscopy, a small camera is entered and directed through the gastroin- testinal tract to detect and remove polyps. But typical video endoscopy takes long period of time generating a long video. So, as an operator dependent procedure, it is not possible for a medical person to examine it with sufcient attentiveness during such long and back-to-back endoscopy. However, accuracy of the diagnosis depends on doctor’s experience. So, in the examination, some polyps may be undetected. Tis misdetection of polyps can lead to malignant tumors in the future. Computer aided polyp detection system can reduce polyp misdetection rate and assists doctors in fnding the most important regions to be analyzed. Such system can support diagnosis procedure by detecting polyps, classifying polyps, and generating detailed report about any part that should be examined with more attention. Again, duration of this uncomfortable process for the patients and the cost of operation can also be reduced. A large number of methods have been proposed and applied for computer aided polyp detection system. Covari- ances of the second-order statistical measures over the wavelet frame transformation (CWC) of diferent color bands have been used as the image features in [1] for colonoscopy tumor detection with 97% specifcity and 90% sensitivity. In their consecutive work [2], an intelligent system of SVM and color-texture analysis methodologies was developed having accuracy of 94%. Adaptive neurofuzzy-based approach for Hindawi International Journal of Biomedical Imaging Volume 2017, Article ID 9545920, 9 pages https://doi.org/10.1155/2017/9545920