A. Balamurugan and D. Surendran, Department of CSE, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
V.S. Vaisakhi, Department of ECE, Nehru Institute of Engineering and Technology, Coimbatore, India
S. Umamaheswari, Software Architect, MicroFocus, Bangalore, India
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :477-481
Abstract
Diabetic retinopathy is an eye condition that can cause vision loss and blindness in people who have diabetics. It affects blood vessels in the retina. Initially, Diabetic retinopathy may not have any symptoms, but finding it early can help us to take steps to protect our vision. Some people notice changes in their vision, like trouble in reading or seeing faraway objects, these changes may come and go. In later stages of diseases, blood vessels in the retina starts to bleed into the vitreous. If this happens, you may see dark, floating spots or streaks that look like lobwels. Sometimes the spots clear up on their own, but it is important to start the treatment, otherwise it may get worse and the bleeding can happen again. There are various stages, it includes blurred vision, impairment of color vision, floaters, patches or streaks. Hence in our project, we came up with an idea of identifying diabetic retinopathy in early stages, to classify a given set of images into four classes, we are using supervised learning methods. For this task, we use deep learning technique with inception v3module along with skin locus model in order to achieve better results and for easy classification of images.
Keywords
Diabetic Retinopathy; deep learning; inception v3.
Cite this article
A. Balamurugan, D. Surendran, V.S. Vaisakhi and S. Umamaheswari, “Diagnosis of Diabetic Retinopathy Using Machine Learning”, Innovations in Information and Communication Technology, pp.477-481, December 2020.
Copyright
© 2020 A. Balamurugan, D. Surendran, V.S. Vaisakhi and S. Umamaheswari. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
