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Title |
Machine Learning Techniques for Diabetic Retinopathy Detection: A Review |
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Author |
Rachna Kumari, Sanjeev Kumar, Sunila Godara |
| Citation |
Vol. 24 No. 4 pp. 67-76 |
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Abstract |
Diabetic retinopathy is a threatening complication of diabetes, caused by damaged blood vessels of light sensitive areas of retina. DR leads to total or partial blindness if left untreated. DR does not give any symptoms at early stages so earlier detection of DR is a big challenge for proper treatment of diseases. With advancement of technology various computer-aided diagnostic programs using image processing and machine learning approaches are designed for early detection of DR so that proper treatment can be provided to the patients for preventing its harmful effects. Now a day machine learning techniques are widely applied for image processing. These techniques also provide amazing result in this field also. In this paper we discuss various machine learning and deep learning based techniques developed for automatic detection of Diabetic Retinopathy. |
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Keywords |
Convolutional neural network, Deep Neural Network, Diabetic retinopathy, k-Nearest Neighbor , Support Vector Machine. |
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