A Review on Detection of Diabetic Retinopathy using Deep Learning and Transfer Learning based Strategies
Keywords:
diabetic retinopathy, deep learning, fundus images, glaucoma, transfer learningAbstract
Diabetic Retinopathy (DR) is considered to be one of the most widely observed and a complex variation of diabetes and stands as a leading cause of blindness globally. The occurrence of DR causes impairment in the retinal blood vessels and leads to unusual growth of blood arteries in the eye. Manual examinations and analysis suggests that the prevalence of DR has been enormously growing at an exponential rate and has already registered for more than 160 million cases worldwide. On the other hand, its diagnostic screening is not only challenging, but also computationally expensive at the same time. Due to the highlighting importance of its early diagnosis in terms of treatment, multiple concepts to DR detection have been used in the past few years. However, research in recent times has resulted in the fact that deep learning based CNN structures and Transfer Learning based MedNets have been popularly used in DR detection, due to its superior performance in the medical domain. As a result of such advancements in Deep Learning methodologies, this article proposes a review on automated approaches used to detect diabetic retinopathy using image processing and disease classification techniques. The review is further preceded with a comprehensive analysis on training a model with an already pre-trained network whose primary goal is to generate useful information and provide it to diabetic researchers, medical practitioners and patients.
References
Mellitus D., “Diagnosis and classification of diabetes mellitus. Diabetes Care”, 2005, 28, S5–S10
Fong, D.S.; Aiello, L.; Gardner, T.W.; King, G.L.; Blankenship, G.; Cavallerano, J.D.; Ferris, F.L.; Klein, R. “Retinopathy in diabetes. Diabetes Care”, 2004, 27, s84–s87
Wilkinson, C.; Ferris, F.L.; Klein, R.E.; Lee, P.P.; Agardh, C.D.; Davis, M.; Dills, D.; Kampik, A.; Pararajasegaram, R.; Verdaguer, J.T. “Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales”, Ophthalmology 2003, 110, 1677–1682
Kempen, J.H.; O’Colmain, B.J.; Leske, M.C.; Haffner, S.M.; Klein, R.; Moss, S.E.; Taylor, H.R.; Hamman, R.F. “The prevalence of diabetic retinopathy among adults in the United States.”, Arch. Ophthalmol. 2004, 122, 552–563
Grzybowski, A.; Brona, P.; Lim, G.; Ruamviboonsuk, P.; Tan, G.S.W.; Abramoff, M.; Ting, D.S.W. “Artificial intelligence for diabetic retinopathy screening: A review.”, Eye 2020, 34, 451–460
Ting, D.S.W.; Cheung, C.M.G.; Wong, T.Y. “Diabetic retinopathy: Global prevalence, major risk factors, screening practices and public health challenges: A review.”, Clin. Exp. Ophthalmol. 2016, 44, 260–277
K. Xu, D. Feng, H. Mi “Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image Molecules”, 22 (12) (2017), p. 2054
G. Quellec, K. Charrière, Y. Boudi, B. Cochener, M. Lamard, “Deep image mining for diabetic retinopathy screening Med Image Anal”, 39 (2017), pp. 178-193
M.T. Esfahani, M. Ghaderi, R. Kafiyeh, “Classification of diabetic and normal fundus images using new deep learning method Leonardo Electron J Pract Technol”, 17 (32) (2018), pp. 233-248
R. Pires, S. Avila, J. Wainer, E. Valle, M.D. Abramoff, A. Rocha, “A data-driven approach to referable diabetic retinopathy detection Artif Intell Med”, 96 (2019), pp. 93-106
H. Jiang, K. Yang, M. Gao, D. Zhang, H. Ma, W. Qian “An interpretable ensemble deep learning model for diabetic retinopathy disease classification”, 2019, 41st Annual International conference of the IEEE engineering in medicine and biology society (EMBC) (2019), pp. 2045-2048
Y.P. Liu, Z. Li, C. Xu, J. Li, R. Liang, “Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network”, Artif Intell Med, 99 (2019), p. 101694
V. Gulshan, et al. “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”, Am Med Assoc, 316 (2016), pp. 2402-2410
M.D. Abràmoff, et al. “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning Investig”, Ophthalmol Vis Sci, 57 (13) (2016), pp. 5200-5206
H. Pratt, F. Coenen, D.M. Broadbent, S.P. Harding, Y. Zheng, “Convolutional neural networks for diabetic retinopathy”, Procedia Comput Sci, 90 (2016), pp. 200-205
T. Li, Y. Gao, K. Wang, S. Guo, H. Liu, H. Kang “Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening”, Inf Sci, 501 (2019), pp. 511-522
J.I. Orlando, E. Prokofyeva, M. del Fresno, M.B. Blaschko, “An ensemble deep learning based approach for red lesion detection in fundus images Comput Methods”, Progr Biomed, 153 (2018), pp. 115-127
P. Chudzik, S. Majumdar, F. Calivá, B. Al-Diri, A., “Hunter Microaneurysm detection using fully convolutional neural networks Comput Methods”, Progr Biomed, 158 (2018), pp. 185-192
P. Chudzik, S. Majumdar, F. Calivá, B. Al-Diri, A., “Hunter Microaneurysm detection using fully convolutional neural networks Comput Methods”, Progr Biomed, 158 (2018), pp. 185-192
H. Wang, et al. “Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening Comput Methods”, Progr Biomed, 191 (2020), p. 105398
G. Li, S. Zheng, and X. Li, ‘‘Exudate detection in fundus images via convolutional neural network”, in International Forum on Digital TV and Wireless Multimedia Communications. Singapore: Springer, 2017, pp. 193–202
A. V Vasilakos, Y. Tang, Y. Yao, “Neural networks for computer-aided diagnosis in medicine : a review Neurocomputing”, 216 (2016), pp. 700-708
T. Li, Y. Gao, K. Wang, S. Guo, H. Liu, H. Kang “Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening”, Inf Sci, 501 (2019), pp. 511-522
Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, et al. “A comprehensive survey on transfer learning”, Proc IEEE. 2020;109:43–76
M. Sokolova and G. Lapalme, ‘‘A systematic analysis of performance measures for classification tasks,’’ Inf. Process. Manage., vol. 45, no. 4, pp. 427–437, Jul. 2009
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