[2] [2] Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance［J］. International Journal of Remote Sensing,2007,28(5):823-870.

[3] [3] Murray R F. Classification images: A review［J］. Journal of Vision,2011,11(5):2-2.

[4] [4] Wang P, Li J L, Ding H. Optical coherence tomography image for automatic classification of diabetic macular edema［J］. Optica Applicata,2020,50(4):567-577.

[6] [6] Wimmer G, Vécsei A, Uhl A. CNN transfer learning for the automated diagnosis of celiac disease［C］∥2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). Finland,Oulu:IEEE,2016:1-6.

[7] [7] Rocheteau E, Kim D. Deep transfer learning for automated diagnosis of skin lesions from photographs［J］. arXiv preprint arXiv:2011.04475,2020.

[8] [8] An G, Akiba M, Omodaka K, et al. Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images［J］. Scientific Reports,2021,11(1):1-9.

[9] [9] Karri S P K, Chakraborty D, Chatterjee J. Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration［J］. Biomedical Optics Express,2017,8(2):579-592.

[10] [10] Kermany D S, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning［J］. Cell,2018,172(5):1122-1131.

[11] [11] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition［J］. arXiv preprint arXiv:1409.1556,2014.

[12] [12] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision［C］∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nv,USA:CVPR,2016:2818-2826.

[13] [13] Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City,USA:CVPR,2018:4510-4520.

[14] [14] Ma N, Zhang X, Zheng H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design［C］∥Proceedings of The European Conference on Computer Vision. Munich,Germany:ECCV,2018:116-131.

[19] [19] Li Y, Wang X, Shi B, et al. Hand gesture recognition using IR-UWB radar with ShuffleNet V2［C］∥Proceedings of the 5th International Conference on Control Engineering and Artificial Intelligence. Sanya,China:CCEAI,2021:126-131.

[20] [20] Chen C, Zhu T, Li S, et al. Apple leaf disease regcognition method base on improved ShuffleNet V2［C］∥2021 3rd International Conference on Advances in Computer Technology,Information Science and Communication(CTISC).Shanghai,China:IEEE,2021:276-282.

[23] [23] Hussain M A, Bhuiyan A, D Luu C, et al. Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm［J］. PloS one,2018,13(6): e0198281.