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Automated Detection of Diabetic Retinopathy using Deep Residual Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Md Ashikur Rahman, Md Arifur Rahman, Juena Ahmed Noshin

Md Ashikur Rahman, Md Arifur Rahman and Juena Ahmed Noshin. Automated Detection of Diabetic Retinopathy using Deep Residual Learning. International Journal of Computer Applications 177(42):25-32, March 2020. BibTeX

	author = {Md Ashikur Rahman and Md Arifur Rahman and Juena Ahmed Noshin},
	title = {Automated Detection of Diabetic Retinopathy using Deep Residual Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2020},
	volume = {177},
	number = {42},
	month = {Mar},
	year = {2020},
	issn = {0975-8887},
	pages = {25-32},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2020919927},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Significant amount of people suffer from Diabetic Retinopathy (DR), which is one of the major causes of vision loss. The incidence of this disease is even higher due to not being diagnosed at the right time. On numerous occasions, due to neglect and poor care, diabetic retinopathy can lead to significant damage to the eyes. That is why, early diagnosis of eye diseases, proper treatment and care for the disease can prevent vision loss. Referral of eyes with diabetic retinopathy for advanced assessment and treatment would aid in reducing the chances of vision loss, allowing proper diagnoses. The purpose of this study is to develop resilient and flexible diagnostic techniques for the detection of DR and to identify dynamic DR grading using residual networks to facilitate the network training that are significantly intense than previously used networks. Even though lots of research has been done on DR, its identifications remains challenging due to time and space complexity along with higher accuracy specificity. Here, a residual learning framework has been proposed that overcomes the challenges while efficiently detecting DR. Hence, using a high-end Graphics Processor Unit (GPU) the model has been trained on the publicly available Kaggle dataset and empirical evidence has been provided in order to support the results with a sensitivity of 95.6% and an accuracy of 93.20%.


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Diabetic Retinopathy, Deep Neural Network, Residual Learning, Image Recognition