[1] PRIYA R,ARUNA P. Review of automated diagnosis of diabetic retinopathy using the support vector machine[J]. International Journal of Applied Engineering Research, 2011, 1(4):844-862. [2] PRIYA R, ARUNA P. SVM and neural network based diagnosis of diabetic retinopathy[J]. International Journal of Computer Applications,2012,41(1):6-12. [3] 丁蓬莉.基于深度学习的糖尿病性视网膜分析算法研究[D].北京:北京交通大学,2017:22-23. (DING P L. Research of diabetic retinal image analysis algorithms based on deep learning[D]. Beijing:Beijing Jiaotong University, 2017:22-23.) [4] 蔡石林.基于CNN的糖尿病视网膜病变识别算法研究与实现[D].长沙:湖南大学,2018:22-25. (CAI S L. Research and implementation on diabetic retinopathy recognition algorithm based on CNN[D]. Changsha:Hunan University, 2018:22-25.) [5] 马文俊.基于机器学习的糖尿病视网膜病变分级研究[D].哈尔滨:哈尔滨工程大学,2017:28-31. (MA W J. Study on classification of diabetic retinopathy based on machine learning[D]. Harbin:Harbin Engineering University, 2017:28-31.) [6] 张德彪.基于深度学习的糖尿病视网膜病变分类和病变检测方法的研究[D].哈尔滨:哈尔滨工业大学,2017:25-29. (ZHANG D B. Research on diabetic retinopathy classification and lesion detection based on deep learning[D]. Harbin:Harbin Institute of Technology, 2017:25-29.) [7] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:1440-1448. [8] REN S, HE K, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of the 2015 International Conference on Neural Information Processing Systems. Cambridge, MA:MIT Press, 2015:91-99. [9] DAI J, LI Y, HE K, et al. R-FCN:object detection via region-based fully convolutional networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. North Miami Beach, FL:Curran Associates Inc., 2016:379-387. [10] OQUAB M, BOTTOUB L, LAPTEV I, et al. Learning and transferring mid-level image representations using convolutional neural networks[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2014:1717-1724. [11] OQUAB M, BOTTOUB L, LAPTEV I, et al. Is object localization for free?-weakly-supervised learning with convolutional neural networks[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:685-694. [12] ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Object detectors emerge in deep scene CNNs[J]. arXiv E-print, 2015:arXiv:1412.6856. [13] ZHOU B, KHOSLA A, LAPEDRIZA, OLIVA A, et al. Learning deep features for discriminative localization[C]//Proceedings of the 2016 the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC:IEEE Computer Society, 2016:2921-2929. [14] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:1-9. [15] LIN M, CHEN Q, YAN S. Network in network[J]. arXiv E-print, 2014:arXiv:1312.4400. [16] SZEGEDY C, VANHOUCKE V, LOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:2818-2826. |