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W. Baxt (1995)
Application of artificial neural networks to clinical medicineThe Lancet, 346
E. Ribeiro, A. Uhl, Georg Wimmer, M. Häfner (2016)
Exploring Deep Learning and Transfer Learning for Colonic Polyp ClassificationComputational and Mathematical Methods in Medicine, 2016
E. Ribeiro, A. Uhl, M. Häfner (2016)
Colonic Polyp Classification with Convolutional Neural Networks2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)
D. West, Vivian West (2000)
Model selection for a medical diagnostic decision support system: a breast cancer detection caseArtificial intelligence in medicine, 20 3
P. Mesejo, Daniel Pizarro-Perez, A. Abergel, O. Rouquette, S. Béorchia, L. Poincloux, A. Bartoli (2016)
Computer-Aided Classification of Gastrointestinal Lesions in Regular ColonoscopyIEEE Transactions on Medical Imaging, 35
Xiao Jia, M. Meng (2016)
A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Sun Park, D. Sargent (2016)
Colonoscopic polyp detection using convolutional neural networks, 9785
Robert Hawlick
Statistical and Structural Approaches to Texture
Jorge Bernal, Nima Tajkbaksh, F. Sánchez, B. Matuszewski, Hao Chen, Lequan Yu, Quentin Angermann, O. Romain, Bjørn Rustad, I. Balasingham, Konstantin Pogorelov, Sungbin Choi, Quentin Debard, L. Maier-Hein, S. Speidel, D. Stoyanov, P. Brandao, H. Córdova, C. Sánchez-Montes, S. Gurudu, G. Fernández-Esparrach, X. Dray, Jianming Liang, A. Histace (2017)
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision ChallengeIEEE Transactions on Medical Imaging, 36
Rongsheng Zhu, Rong Zhang, Dixiu Xue (2015)
Lesion detection of endoscopy images based on convolutional neural network features2015 8th International Congress on Image and Signal Processing (CISP)
Salih Göktürk, Carlo Tomasi, B. Acar, C. Beaulieu, D. Paik, R. Jeffrey, J. Yee, S. Napel (2001)
A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonographyIEEE Transactions on Medical Imaging, 20
R. Haralick, K. Shanmugam, I. Dinstein (1973)
Textural Features for Image ClassificationIEEE Trans. Syst. Man Cybern., 3
Luís Alexandre, N. Nobre, João Casteleiro (2008)
Color and Position versus Texture Features for Endoscopic Polyp Detection2008 International Conference on BioMedical Engineering and Informatics, 2
Shutao Li, J. Kwok, Hailong Zhu, Yaonan Wang (2003)
Texture classification using the support vector machinesPattern Recognit., 36
I. El-Naqa, Yongyi Yang, M. Wernick, N. Galatsanos, R. Nishikawa (2002)
A support vector machine approach for detection of microcalcificationsIEEE Transactions on Medical Imaging, 21
Nima Tajbakhsh, S. Gurudu, Jianming Liang (2015)
Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
Dimitrios Iakovidis, D. Maroulis, S. Karkanis (2006)
An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopyComputers in biology and medicine, 36 10
V. Kodogiannis, M. Boulougoura (2007)
An adaptive neurofuzzy approach for the diagnosis in wireless capsule endoscopy imaging
S. Karkanis, Dimitrios Iakovidis, D. Maroulis, Dimitrios Karras, M. Tzivras (2003)
Computer-aided tumor detection in endoscopic video using color wavelet featuresIEEE Transactions on Information Technology in Biomedicine, 7
Nima Tajbakhsh, S. Gurudu, Jianming Liang (2014)
Automatic Polyp Detection Using Global Geometric Constraints and Local Intensity Variation PatternsMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 Pt 2
Jeff Donahue, Yangqing Jia, O. Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell (2013)
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
A. Krizhevsky, Ilya Sutskever, Geoffrey Hinton (2012)
ImageNet classification with deep convolutional neural networksCommunications of the ACM, 60
Yuexian Zou, Lei Li, Yi Wang, Jiasheng Yu, Yi Li, W. Deng (2015)
Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network2015 IEEE International Conference on Digital Signal Processing (DSP)
Baopu Li, Yichen Fan, M. Meng, Lin Qi (2009)
Intestinal polyp recognition in capsule endoscopy images using color and shape features2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)
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.
Biomedical Engineering Letters – Springer Journals
Published: Sep 7, 2017
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