Age-related Macular Degeneration detection using deep convolutional neural network
Tài liệu tham khảo
Department of Economic and Social Affairs, World Population Prospects The 2017 Revision Key Findings and Advance Tables, United Nations, New York, 2017.
Prince, 2015, The burden of disease in older people and implications for health policy and practice, Lancet, 385, 549, 10.1016/S0140-6736(14)61347-7
Prenner, 2015, Disease burden in the treatment of age-related macular degeneration: findings from a time-and-motion study, Am. J. Ophthalmol., 160, 725, 10.1016/j.ajo.2015.06.023
World Health Organization, World report on ageing and health, 2017.
National Eye Institute, Facts about age-related macular degeneration, 2015 (Online). Available: https://nei.nih.gov/health/maculardegen/armd_facts. (Accessed 28 July 2017).
Koh, 2018, Automated retinal health diagnosis using pyramid histogram of visual words and fisher vector techniques, Comput. Biol. Med., 92, 10.1016/j.compbiomed.2017.11.019
Koh, 2017, Diagnosis of retinal health in digital fundus images using continuous wavelet transform (cwt) and entropies, Comput. Biol. Med., 84, 10.1016/j.compbiomed.2017.03.008
Koh, 2017, Automated detection of retinal health using phog and surf features extracted from fundus images, Appl. Intell., 1
Lim, 2012, Age-related macular degeneration, Lancet, 379, 1728, 10.1016/S0140-6736(12)60282-7
S.J. Ryan, A.P. Schachat, C.P. Wilkinson, D.R. Hinton, S.R. Sadda, P. Wiedemann, Retina, 5th ed. Expert Consult Premium Edition: Enhanced Online Features and Print, 3-Volume Set, 2012.
Lee, 2017, Deep learning in medical imaging: general overview, Korean J. Radiol., 18, 570, 10.3348/kjr.2017.18.4.570
Greenspan, 2016, Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique, IEEE Trans. Med. Imaging, 35, 1153, 10.1109/TMI.2016.2553401
Van Grinsven, 2016, Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images, IEEE Trans. Med. Imaging, 35, 1273, 10.1109/TMI.2016.2526689
Tan, 2017, Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network, J. Comput. Sci., 20, 70, 10.1016/j.jocs.2017.02.006
Acharya, 2017, Automated detection of arrhythmias using different intervals of tachycardia ecg segments with convolutional neural network, Inf. Sci. (Ny)., 405, 10.1016/j.ins.2017.04.012
Acharya, 2017, Automated detection of coronary artery disease using different durations of ecg segments with convolutional neural network, Knowledge-Based Syst., 946, 1
Acharya, 2017, Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals, Inf. Sci. (Ny)., 416, 190, 10.1016/j.ins.2017.06.027
K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE International Conference on Computer Vision, vol. 11–18–Dec, 2016, pp. 1026–1034.
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 1
Zhao, 2017, Convolutional neural networks for time series classification, J. Syst. Eng. Electron., 28, 162, 10.21629/JSEE.2017.01.18
Bouvrie, 2006, Notes on convolutional neural networks, In Pract., 12, 47
Kingma, 2015, 1
S. Ruder, An overview of gradient descent optimization algorithms, Web Page, 2016, pp. 1–12.
Theano Development Team, Theano: A Python framework for fast computation of mathematical expressions, 2016. ArXiv e-prints, p. 19.
Van Stralen, 2009, Diagnostic methods I: sensitivity, specificity, and other measures of accuracy, Kidney Internat., 75, 1257, 10.1038/ki.2009.92
Devroye, 1979, Distribution-free performance bounds for potential function rules, IEEE Trans. Inform. Theory, 25, 601, 10.1109/TIT.1979.1056087
Duda, 2001
Köse, 2008, Automatic segmentation of age-related macular degeneration in retinal fundus images, Comput. Biol. Med., 38, 611, 10.1016/j.compbiomed.2008.02.008
Köse, 2010, A statistical segmentation method for measuring age-related macular degeneration in retinal fundus images, J. Med. Syst., 34, 1, 10.1007/s10916-008-9210-4
Acharya, 2017, Automated screening tool for dry and wet age-related macular degeneration (armd) using pyramid of histogram of oriented gradients (phog) and nonlinear features, J. Comput. Sci., 20, 10.1016/j.jocs.2017.03.005
Ferris, 2013, Clinical classification of age-related macular degeneration, Ophthalmology, 120, 844, 10.1016/j.ophtha.2012.10.036
Agurto, 2011, Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images, Invest. Ophthalmol. Vis. Sci., 52, 5862, 10.1167/iovs.10-7075
Zheng, 2012, Automated ‘disease/ no disease’ grading of age-related macular degeneration by an image mining approach, Invest. Ophthalmol. Vis. Sci., 53, 10.1167/iovs.12-9576
Hijazi, 2012, Data mining techniques for the screening of age-related macular degeneration, Knowl.-Based Syst., 29, 83, 10.1016/j.knosys.2011.07.002
Hijazi, 2014, Data mining for amd screening: a classification based approach, Int. J. Simul. Syst. Sci. Technol., 15, 57
Mookiah, 2014, Automated diagnosis of age-related macular degeneration using greyscale features from digital fundus images, Comput. Biol. Med., 53, 55, 10.1016/j.compbiomed.2014.07.015
M.R.K., 2014, Decision support system for age-related macular degeneration using discrete wavelet transform, Med. Biol. Eng. Comput., 52, 781, 10.1007/s11517-014-1180-8
Mookiah, 2015, Automated detection of age-related macular degeneration using empirical mode decomposition, Knowledge-Based Syst., 89, 10.1016/j.knosys.2015.09.012
Mookiah, 2015, Local configuration pattern features for age-related macular degeneration characterization and classification, Comput. Biol. Med., 63, 208, 10.1016/j.compbiomed.2015.05.019
Acharya, 2016, Novel risk index for the identification of age-related macular degeneration using radon transform and dwt features, Comput. Biol. Med., 73, 10.1016/j.compbiomed.2016.04.009
Yann, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Adhi, 2013, Optical coherence tomography-current and future applications, Curr. Opin. Ophthalmol., 24, 213, 10.1097/ICU.0b013e32835f8bf8