Abstract
Traditional image object classification algorithms and strategies are difficult to meet the requirements of image processing efficiency, performance, and intelligence. In recent years, deep learning in the computer vision has made great progress, showing good application prospects in medical image reading. Firstly, the background of deep learning and the knowledge of convolutional neural networks are introduced to fundamentally understand the basic model architecture and optimization methods of deep learning applied in the medical image. Secondly, the classification method of diabetic retinopathy images is discussed specifically. Finally, the problems faced in the future are analyzed.
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References
H. Pratt, F. Coenen, D.M. Broadbent, et al., Convolutional neural networks for diabetic retinopathy. Proc. Comput. Sci. 90, 200–205 (2016)
K. Shankar, M. Elhoseny, S.K. Lakshmanaprabu, M. Ilayaraja, R.M. Vidhyavathi, M. Alkhambashi, Optimal feature level fusion based ANFIS classifier for brainMRI image classification, in Concurrency and Computation: Practice and Experience (Wiley, Hoboken, 2018)
M. Elhoseny, K. Shankar, S.K. Lakshmanaprabu, A. Maseleno, N. Arunkumar, Hybrid optimization with cryptography encryption for medical image security in Internet of Things, in Neural Computing and Applications (Springer, New York, 2018). https://doi.org/10.1007/s00521-018-3801-x
K. Shankar, M. Elhoseny, R. Satheesh Kumar, S. K. Lakshmanaprabu, X. Yuan, Secret image sharing scheme with encrypted shadow images using optimal homomorphic encryption technique. J. Ambient Intell. Humanized Comput. 2018. https://doi.org/10.1007/s12652-018-1161-0
M. Elhoseny, G.-B. Bian, S.K. Lakshmanaprabu, K. Shankar, A.K. Singh, W. Wu, Effective features to classify ovarian cancer data in internet of medical things. Comput. Netw. 159, 147–156 (2019)
E. Mohamed, K. Shankar, J. Uthayakumar, Intelligent diagnostic prediction and classification system for chronic kidney disease. Nat. Sci. Rep. 9, 9583 (2019). https://doi.org/10.1038/s41598-019-46074-2
N. Krishnaraj, M. Elhoseny, M. Thenmozhi, M.M. Selim, K. Shankar, Deep learning model for real-time image compression in Internet of Underwater Things (IoUT). J. Real-Time Image Process. 2019. https://doi.org/10.1007/s11554-019-00879-6
N. Srivastava, G. Hinton, A. Krizhevsky, et al., Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift. in International Conference on International Conference on Machine Learning. JMLR.org, 2015
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in International Conference on Neural Information Processing Systems (Curran Associates Inc., Red Hook, 2012), pp. 1097–1105
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014
K. He, X. Zhang, S. Ren, et al., Deep residual learning for image recognition. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, 2016
J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks. 2017
S. Xie, Z. Tu, Holistically-nested edge detection. Int. J. Comput. Vis. 125(1-3), 3–18 (2015)
H. Fu, Y. Xu, D.W.K. Wong, et al., Retinal vessel segmentation via deep learning network and fully-connected conditional random fields, in IEEE, International Symposium on Biomedical Imaging (IEEE, Piscataway, 2016), pp. 698–701
X. Yuan, L. Gu, T. Chen, M. Elhoseny, W. Wang, A fast and accurate retina image verification method based on structure similarity. in 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications, pp. 181–185, 2018
L. Li, M. Fredrikson, S. Sen, et al., Case study: explaining diabetic retinopathy detection deep CNNs via integrated gradients. 2017
P. Ding, Q. Li, Z. Zhang, F. Li, Diabetic retinal image classification method based on deep neural network. J. Comput. Appl. 37(3), 699–704 (2017)
H. Pang, C. Wang, Deep learning model for diabetic retinopathy detection. J. Softw. 28(11), 3018–3029 (2017)
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Yu, X., Liu, J., Xue, W. (2020). A Review of the Application of Deep Learning in the Classification of Diabetic Retinopathy. In: Yuan, X., Elhoseny, M. (eds) Urban Intelligence and Applications. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-45099-1_11
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