Abstract
Diabetic retinopathy is referred to as eye sight damage and permanent blindness because of diabetic condition in humans. Diabetic patients are growing up in numbers every year around the globe. Because of modern day life style with elevated stress and tension the risk is even higher. Hence it is crucial for diabetic patients to look for its effect on other body parts as diabetic condition hit eye more likely and if left unchecked, may lead to serious eye related issues. But it can be easily monitored by regular checkups and proper health care and prevented from further degradation, while an automatic screening system to identify whether an individual need follow up or referral for supplementary action to avoid blindness can ease the task of detection at early stages to a great extent. In this paper a model for automatic detection of diabetic retinopathy is proposed using low complexity image processing technique and modified Convolutional Neural Network (CNN) with better accuracy and precision to help an ophthalmologist through detection of change in retina features. The proposed model is used to classify the fundus images into two categories, viz. healthy and infected and tested on EyePACS dataset which obtained classification accuracy of 82% shows the robustness of the system.
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References
WHO. Global report on diabetes (2016)
Chan, J.C., et al.: Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA 301(20), 2129–2140 (2009)
Congdon, N.G., Friedman, D.S., Lietman, T.: Important causes of visual impairment in the world today. JAMA 290(15), 2057–2060 (2003)
Verma, K., Deep, P., Ramakrishnan, A.G.: Detection and classification of diabetic retinopathy using retinal images. In: 2011 Annual IEEE India Conference, Hyderabad, India. https://doi.org/10.1109/INDCON.2011.6139346
National Mental Health Survey of India, 2015–2016, Supported by Ministry of Health and Family Welfare Government of India. http://www.nimhans.ac.in
Cuadros, J., Bresnick, G.: EyePACS: an adaptable telemedicine system for diabetic reti-nopathy screening. J. Diab. Sci. Technol. 3(3), 509–516 (2009)
Mookiah, M., Acharya, U., Chua, C., Lim, C., Ng, E., Laude, A.: Computer-aided diagnosis of diabetic retinopathy: a review. Comput. Biolo. Med. 43(12), 2136–2155 (2013)
Niemeijer, M., et al.: Retinopathy online challenge: auto-matic detection of microaneurysms in digital color fundus photographs. IEEE Trans. Med. Imaging 29(1), 185–195 (2010)
Wang, S., Tang, H.L., Hu, Y., Sanei, S., Saleh, G.M., Peto, T., et al.: Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE Trans. Biomed. Eng. 64(5), 990–1002 (2017)
Abramoff, M.D., et al.: Automated early detection of diabetic retinopathy. Ophthalmology 117(6), 1147–1154 (2010)
Quellec, G., Lamard, M., Josselin, P.M., Cazuguel, G., Cochener, B., Roux, C.: Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans. Med. Imaging 27(9), 1230–1241 (2008)
Noronha, K., Acharya, U.R.: Decision support system for diabetic retinopathy using discrete wavelet transform. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 227(3), 251–261 (2013)
Antal, B., Hajdu, A.: An ensemble-based system for microaneurysm detection and dia-betic retinopathy grading. IEEE Trans. Biomed. Eng. 59(6), 1720–1726 (2012)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)
Rahim, S.S., Palade, V., Shuttleworth, J., Jayne, C.: Automatic screening and classification of diabetic retinopathy fundus images. In: Mladenov, V., Jayne, C., Iliadis, L. (eds.) EANN 2014. CCIS, vol. 459, pp. 113–122. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11071-4_11
Verma, K., Deep, P., Ramakrishnan, A.G.: Detection and Classification of Diabetic Retinopathy Using Retinal Images
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Choudhury, A.R., Bhattacharya, D., Debnath, A., Biswas, A. (2020). An Integrated Image Processing and Deep Learning Approach for Diabetic Retinopathy Classification. In: Saha, A., Kar, N., Deb, S. (eds) Advances in Computational Intelligence, Security and Internet of Things. ICCISIoT 2019. Communications in Computer and Information Science, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-15-3666-3_1
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