AN AUTOMATED APPROACH FOR DETECTING DIABETIC RETINOPATHY USING WORDS GENERATION WITH UPDATED SURF AND KNN
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Keywords
Diabetic Retinopathy (DR), Classification, Retina, Machine Learning
Abstract
Human physiology study has been using image processing on various areas. On the contrary, manual study requires huge amount of time and expertise. The work presented here uses machine learning and image processing in order to diagnose the diabetic retinopathy present in the retinal image taken from the fundus camera. The dataset are being collected from various databases such as MESSIDOR, IDBDR0, and IDBDR1. The methodolgy uses the words generation technique with combination of updated SURF and KNN that classifies over 200 images taken from Fundus camera containing lesions present in the retina of the eye. The proposed technique helps in treating and early diagnosis of DR, helping clinicians in a faster mode.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Pradeep Kumar K.G
Assistant Professor, Dept. of Computer Science & Engineering, A. J. Institute of Engineering & Technology, Mangalore.
Dr. Karunakara K
Professor & Head, Dept. of ISE, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka.
Dr. Thyagaraju G. S
Professor & Head, Dept. of CSE, SDM Institute of Technology, Ujire, India.