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Diabetes Retinopathy Detection : A Survey


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1 Post Graduate Student at Department of Information Technology, PSG College of Technology, Coimbatore - 641 004, Tamil Nadu, India
2 Assistant Professor at Department of Information Technology, PSG College of Technology, Coimbatore - 641 004, Tamil Nadu, India

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Diabetic Retinopathy (DR) is a disease that may cause vision impairment. The early detection of this disease is important. This work surveys the different detection and feature selection techniques involved in the detection of this disease. This can be done by studying the lesions found in the human retina using fundus images. This work also reviews the various feature extraction techniques such as Support Vector Machines, Neural Networks, and Convolutional Networks. Deep learning algorithms are discussed and the different ways of implementation of the automated system are discussed.

Keywords

Convolutional Neural Networks, Deep Learning, Diabetes Retinopathy, Feature Selection, Lesions, Fundus Images, Neural Networks, Support Vector Machines, Vision Impairment.

Manuscript Received: January 8, 2020; Revised: January 16, 2020; Accepted: January 18, 2020. Date of Publication: February 5, 2020.

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  • Diabetes Retinopathy Detection : A Survey

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Authors

K. Khavya
Post Graduate Student at Department of Information Technology, PSG College of Technology, Coimbatore - 641 004, Tamil Nadu, India
S. P. Rajamohana
Assistant Professor at Department of Information Technology, PSG College of Technology, Coimbatore - 641 004, Tamil Nadu, India

Abstract


Diabetic Retinopathy (DR) is a disease that may cause vision impairment. The early detection of this disease is important. This work surveys the different detection and feature selection techniques involved in the detection of this disease. This can be done by studying the lesions found in the human retina using fundus images. This work also reviews the various feature extraction techniques such as Support Vector Machines, Neural Networks, and Convolutional Networks. Deep learning algorithms are discussed and the different ways of implementation of the automated system are discussed.

Keywords


Convolutional Neural Networks, Deep Learning, Diabetes Retinopathy, Feature Selection, Lesions, Fundus Images, Neural Networks, Support Vector Machines, Vision Impairment.

Manuscript Received: January 8, 2020; Revised: January 16, 2020; Accepted: January 18, 2020. Date of Publication: February 5, 2020.


References





DOI: https://doi.org/10.17010/ijcs%2F2020%2Fv5%2Fi1%2F151312