[1] Smitha P, Shaji L, Mini M G. A Review of Medical Image Classification Techniques[C]∥International conference on VLSI, Communications and instrumentation, Karunagapally. International Journal of Computer Applications, 2011, 34-38.

[2] Yang J L, Zhao J J, Qiang Y et al. A classification method of pulmonary nodules based on deep belief network[J]. Science Technology and Engineering, 16, 69-74(2016).

[3] Li Y Q, Qiu J F, Zhang W M et al[M]. Medical imaging theory(2010).

[4] Lü X Q, Wu L, Gu Y et al. Low dose CT lung denoising model based on deep convolution neural network[J]. Journal of Electronics & Information Technology, 40, 1353-1359(2018).

[5] Bamber J C, Daft C. Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images[J]. Ultrasonics, 24, 41-44(1986). http://europepmc.org/abstract/MED/3510500

[6] Lu C T, Chen M Y, Shen J H et al. X-ray bio-image denoising using directional-weighted-mean filtering and block matching approach[J]. Journal of Ambient Intelligence & Humanized Computing, 1-18(2018). http://link.springer.com/10.1007/s12652-018-0692-8

[7] Shang X B, Ding Y, Ding R Z et al. A three-dimensional denoising method for low-dose computed tomography[J]. Journal of Medical Imaging & Health Informatics, 7, 283-287(2017). http://www.ingentaconnect.com/content/asp/jmihi/2017/00000007/00000001/art00046

[8] Ali H M. High-resolution neuroimaging-basic physical principles and clinical applications. Chapter 7: MRI medical image denoising by fundamental filters[M]. London:, 111-124(2018).

[9] Zhang X, Gu H B, Zhou L et al. Improved dual-domain filtering and threshold function denoising method for ultrasound images based on non-subsampled contourlet transform[J]. Journal of Medical Imaging and Health Informatics, 7, 1624-1628(2017). http://www.ingentaconnect.com/content/asp/jmihi/2017/00000007/00000007/art00023

[10] Hou Y Y, Zhou P. Approach on digital chest radiographs enhancement based on wavelet transform[J]. Chinese Journal of Medical Imaging Technology, 26, 1976-1979(2010).

[11] Lü L Z, Qiang Y. Medical CT image enhancement algorithm based on Laplacian pyramid and wavelet transform[J]. Computer Science, 43, 300-303(2016).

[12] Anand C S, Sahambi J S. Wavelet domain non-linear filtering for MRI denoising[J]. Magnetic Resonance Imaging, 28, 842-861(2010). http://www.sciencedirect.com/science/article/pii/S0730725X10000767

[13] Wang S B, Guo Y C, Gao M et al. Method of medical ultrasonic image de-noising based on fuzzy PCNN in the wavelet domain[J]. Journal of Optoelectronics·laser, 21, 476-480(2010).

[14] Sun Y Q, Liang X. A new parallel segmentation algorithm for medical image[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8, 139-146(2015).

[15] Saad N M. Abu-Bakar S A R, Muda S, et al. Segmentation of brain lesions in diffusion-weighted MRI using thresholding technique. [C]∥2011 IEEE International Conference on Signal and Image Processing Applications, November 16-18, 2011, Kuala Lumpur, Malaysia. New York: IEEE, 249-254(2012).

[16] Jobin Christ M C, Parvathi R M S. Fuzzy c-means algorithm for medical image segmentation. [C]∥2011 3rd International Conference on Electronics Computer Technology, April 8-10, 2011, Kanyakumari, India. New York: IEEE, 33-36(2011).

[17] Dong C H, Chen Y W, Tateyama T et al. A knowledge-based interactive liver segmentation using random walks. [C]∥2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, August 15-17, 2015, Zhangjiajie, China. New York: IEEE, 1731-1736(2015).

[18] Zou Y, Shuai R J. Improved segmentation algorithm of medical images based on SOM neural network[J]. Computer Engineering and Design, 37, 2533-2537, 2581(2016).

[19] Chen S Y, Chao Y, Zou L. Detection of solitary pulmonary nodules based on geometric features[J]. Journal of Biomedical Engineering, 33, 680-685(2016).

[20] Guo H. Research on solitary lung nodule detection of low dose CT image Xi'an:[D]. Xidian University(2010).

[21] Arias J, Martínez-Gómez J, Gámez J A et al. Medical image modality classification using discrete Bayesian networks[J]. Computer Vision and Image Understanding, 151, 61-71(2016). http://www.sciencedirect.com/science/article/pii/S1077314216300261

[22] Rong J S, Pan H W, Gao L L et al. Medical image multi-stage classification algorithm based on the theory of symmetric[J]. Chinese Journal of Computers, 38, 1809-1824(2015).

[23] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 20, 273-297(1995).

[24] Lü J, Zhu W Y, Qing C et al. Estimation of atmospheric optical turbulence at near surface of Chengdu with support vector machine[J]. Chinese Journal of Lasers, 45, 0404001(2018).

[25] Abdullah N, Ngah U K, Aziz S A. Image classification of brain MRI using support vector machine. [C]∥2011 IEEE International Conference on Imaging Systems and Techniques, May 17-18, 2011, Penang, Malaysia. New York: IEEE, 242-247(2011).

[26] Shen J, Jiang Y, Zhang Y N et al. Multi-class medical image classification approach based on edge detection[J]. Journal of Data Acquisition and Processing, 31, 1028-1034(2016).

[27] Khachane M Y. Organ-based medical image classification using support vector machine[J], 8, 18-30(2017).

[28] Chen C L, Yang W J, Chen Z S. Electrocardiogram computer automatic analysis based on pattern recognition syntax analysis[J]. China Medical Devices Information, 12, 17-18, 22(2006).

[29] Zadeh L A. Fuzzy sets[J]. Information and Control, 8, 338-353(1965).

[30] Zhou L, Jiang Y, Chen N et al. New medical image classify approach based on decision tree twin support vector machine[J]. Computer Engineering and Applications, 52, 76-80(2016).

[31] Hu X W, Jiang Y, Zou L et al. Medical image classification based on neighborhood relation fuzzy rough set[J]. Computer Engineering & Science, 38, 739-746(2016).

[32] El Abbadi N K, Kadhim N E. Brain cancer classification based on features and artificial neural network[J]. International Journal of Advanced Research in Computer and Communication Engineering, 6, 123-134(2017).

[33] Zhou H, Zhang Y S, Gong M. The classification of medical image based on the RBF neural network[J]. International Electronic Elements, 25, 113-116, 120(2017).

[34] Liu F, Gu W J, Wang G T et al. BP neural network based classification method for iris image quality[J]. Shandong Science, 28, 108-112(2015).

[35] Liu Z, Huang J T, Feng X. Action recognition model construction based on multi-scale deep convolution neural network[J]. Optics and Precision Engineering, 25, 799-805(2017).

[36] Setio A A A, Ciompi F, Litjens G et al. . Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks[J]. IEEE Transactions on Medical Imaging, 35, 1160-1169(2016). http://europepmc.org/abstract/MED/26955024

[38] Xue D X. Research on cancer image recognition based on convolutional neural networks[D]. Hefei: University of Science and Technology of China(2017).

[39] Liu Y Y, Tang Q L[J]. Medical image classification based on the multi-scale single-layer SAE The Guide of Science & Education, 2017, 20-21, 192.

[40] Wei C C. Research on medical image classification method based on convolutional neural network[D]. Harbin: Harbin Institute of Technology(2017).

[41] Zhang J, Jiang Y, Hu X W et al. A new medical image classification method based on convolution restricted Holtzman machine[J]. Computer Engineering & Science, 39, 323-329(2017).

[42] Roth H R, Lee C T, Shin H C et al. Anatomy-specific classification of medical images using deep convolutional nets. [C]∥2015 IEEE 12th International Symposium on Biomedical Imaging, April 16-19, 2015, New York. New York: IEEE, 101-104(2015).

[43] Khan S, Yong S P. A deep learning architecture for classifying medical images of anatomy object. [C]∥2017 Asia-Pacific Signal and Information Processing Association Summit and Conference, December 12-15, 2017, Malaysia., 1661-1668(2017).

[44] Yu Y, Lin H, Meng J et al. Deep transfer learning for modality classification of medical images[J]. Information, 8, 91(2017).

[45] Yang N, Nan L, Zhang D Y et al. Research on image interpretation based on deep learning[J]. Infrared and Laser Engineering, 47, 18-25(2018).

[46] Feng X Y, Mei W, Hu D S. Aerial target detection based on improved faster R-CNN[J]. Acta Optica Sinica, 38, 0615004(2018).

[47] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, USA. New York: IEEE, 770-778(2016).

[48] Zhang M, Lü X Q, Wu L et al. Multiplicative denoising method based on deep residual learning[J]. Laser & Optoelectronics Progress, 55, 031004(2018).

[49] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. [C]∥31st Conference on Neural Information Processing Systems, 2017, Long Beach, CA, USA., 3856-3866(2017).

[50] Chang L, Deng X M, Zhou M Q et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 42, 1300-1312(2016).