Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method
10.1007/s13534-017-0048-x
- Author:
Mustain BILLAH
1
;
Sajjad WAHEED
Author Information
1. Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University, Tangail, Bangladesh. mustainbillahx@gmail.com
- Publication Type:Original Article
- Keywords:
Endoscopic image;
Video endoscopy;
Convolutional neural network (CNN);
Color wavelet features;
Support vector machine (SVM);
Improved method
- MeSH:
Endoscopy;
Humans;
Methods;
Polyps;
Sensitivity and Specificity;
Support Vector Machine
- From:
Biomedical Engineering Letters
2018;8(1):69-75
- CountryRepublic of Korea
- Language:English
-
Abstract:
Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.
