Main Article Content

Abstract

Diabetes is a very common disease worldwide. It is primary cause of blindness for people having age less than 50 years. Diabetic retinopathy is a common retinal complication associated with diabetes. Screening and classifying malignancies in diabetic retinopathy is a challenging task because it is symptomatic. Several techniques are available for the identification of abnormality. In this paper algorithms based on deep learning and convolution neural networks are reviewed for diagnosis of Diabetic Retinopathy. A comparison of all methods based on preprocessing techniques, types of feature extraction, convolution neural network architectures and performance metrics is presented. It is observed that Accuracy of any technique depends upon various factors like number of preprocessing steps, number of features used, and number of convolution layers, number and type of data sets used for training the model at the cost of complexity.

Keywords

Diabetic Retinopathy; Deep Learning; Convolution Neural Network; Microneurysms; Hemorrhages; Exudates.

Article Details

How to Cite
[1]
Shital N. Firke and Ranjan Bala Jain, “RECENT ADVANCES IN AUTOMATED DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING DEEP LEARNING”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 5, p. 11, Jun. 2020.

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