Exploration of Retinopathy Disease using Machine Learning Methodology
Khasanah1, Sumardiyono2, Phong Thanh Nguyen3, E. Laxmi Lydia4, K. Shankar5
1Khasanah, STMIK Indonesia Jakarta, Jakarta Pusat, Indonesia.
2Sumardiyono, Department of Public Health, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Indonesia.
3Phong Thanh Nguyen, Department of Project Management, Ho Chi Minh City Open University, Vietnam.
4E.Laxmi Lydia, Vignan’s Department of Computer Science and Engineering, Institute of Information Technology (A),Visakhapatnam (Andhra Pradesh), India.
5K.Shankar, Department of Computer Applications, Alagappa University, India.
Manuscript received on 18 August 2019 | Revised Manuscript received on 29 August 2019 | Manuscript Published on 06 September 2019 | PP: 914-921 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11730886S19/19©BEIESP | DOI: 10.35940/ijeat.F1173.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The whole world is affected with the problem of Diabetic Retinopathy. Whenever a patient has diabetes, it starts affects human body sensitive parts. So the situation becomes very dangerous for the person. Here in this research work it is tried to detect Hemorrhages and micro aneurysms in multiple fundus images collected from various research institutes worldwide and available datasets. In initial it is required to separate RGB colors from the image. The green color is used for further processing. Further the grey color image is extracted for getting the texture of the input image. The feature extraction algorithms are used to classification. So that it is possible to predict the current situation of the retinal image. Once the situation is classified the segmentation algorithms are used using adaptive thresholding segmentation.
Keywords: Diabetic Retinopathy, Segmentation Algorithm, Grey Scale Image, RGB Images.
Scope of the Article: Machine Learning