Abstract
Colorectal cancer is a type of malignant from the intestinal tract. The accurate diagnosis of colorectal polyps can effectively guarantee the life safety of potential patients. There are supervised radionics methods and deep learning methods when determining whether polyps exist. This paper proposes to obtain global features set from computed tomographic colonography (CTC) images by radionics methods and the local features set using deep convolutional neural network simultaneously. Specifically, we use the chaotic evolution algorithm to optimize the parameters in the support vector machine classifier and random forest classifier. Finally, our hybrid method achieved better classification result by random forest classifier on combinational features in which accuracy is 91.318% from the experiment.
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References
De Haan, M.C., P.J. Pickhardt, and J. Stoker. 2015. CT colonography: Accuracy, acceptance, safety and position in organised population screening. Gut 64 (2): 342–350.
Li, J., J. Sun, and L. Liu. 2019. Improved maximum margin clustering via the Bundle method. IEEE Access 7: 63709–63721.
Fan, L., B. Song, X. Gu, and Z. Liang. 2013. Semi-supervised graph embedding-based feature extraction and adaptive kernel-based classification for computer-aided detection in CT colonography. In IEEE Nuclear Science Symposium Conference Record, 3983–3988. IEEE Press.
Shin, Y., H.A. Qadir, L. Aabakken, J. Bergsland, and I. Balasingham. 2018. Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access 6: 40950–40962.
Xu, X., L. Zhang, and J. Li. 2019. A hybrid global-local representation CNN model for automatic cataract grading. IEEE Journal of Biomedical and Health Informatics.
Ning, Z., J. Luo, Y. Li, S. Han, Q. Feng, Y. Xu, W. Chen, T. Chen, and Y. Zhang. 2018. Pattern classification for gastrointestinal stromal tumors by integration of radiomics and deep convolutional features. IEEE Journal of Biomedical and Health Informatics p. 1.
Yan, Z., Y. Zhan, S. Zhang, D. Metaxas, and X.S. Zhou. 2017. Deep learning for medical image analysis || multi-instance multi-stage deep learning for medical image recognition. Deep Learning for Medical Image Analysis pp. 83–104.
Yang, J.J., J. Li, and R. Shen. 2016. Exploiting ensemble learning for automatic cataract detection and grading. Computer Methods and Programs in Biomedicine 124: 45–57.
Kira, Kenji, and Larry A. Rendell. 1992. Feature selection problem: Traditional methods and a new algorithm. In Proceedings Tenth National Conference on Artificial Intelligence, pp. 129–134.
Pei, Y. 2014. Chaotic evolution: Fusion of chaotic ergodicity and evolutionary iteration for optimization. Natural Computing 13 (1): 79–96.
Dalal, N., and B. Triggs. 2005. Histograms of oriented gradients for human detection. In Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, 886–893.
Acknowledgements
Supported by the Beijing Natural Science Foundation under Grant 4184082, in part by the National Natural Science Foundation of China under Grant 61806014.
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Zhan, X., Li, J., Pei, Y. (2020). A Computer-Aided Diagnostic System to Detect Polyp in Computed Tomographic Colonography Images. In: Yang, CT., Pei, Y., Chang, JW. (eds) Innovative Computing. Lecture Notes in Electrical Engineering, vol 675. Springer, Singapore. https://doi.org/10.1007/978-981-15-5959-4_1
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DOI: https://doi.org/10.1007/978-981-15-5959-4_1
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