An intelligent system for gastrointestinal polyp detection in endoscopic video using fusion of bidimensional empirical mode decomposition and convolutional neural network features. Issue 1 (6th June 2019)

• Record Type: Journal Article
• Authors: Mostafiz, Rafid; Hasan, Mosaddik; Hossain, Imran; Rahman, Mohammad M.
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 1(2020)
• Page Start: 224
• Page End: 233
• Publication Date: 2019-06-06
• Subjects: CEMD features -- convolutional neural network (CNN) -- empirical mode decomposition -- support vector machine (SVM) -- video endoscopy; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22350
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: This paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer‐aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%.