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DIABETIC RETINOPATHY DETECTION USING TRANSFER LEARNING
Lakshmi Govind [1], Dharmendra Kumar [2]

Published in: Journal for Advance Research in Applied Sciences
Volume- 4, Issue-1, pp.463-471, Jun 2017
DPI :-> 16.10089.JARAS.2017.V4I1.463471.1756



Abstract
Transfer of knowledge increases the performance of deep learning-the technique used for image classification tasks, including automated diabetic retinopathy screening. Deep learning greed for large amounts of training data poses a challenge for medical tasks, which we can alleviate by recycling knowledge from models trained on different tasks, in a scheme called transfer learning. Although much of transfer learning, a systematic evaluation was not there. Here we investigate the presence of transfer, from which task the transfer is sourced, and the application of the fine tuning. The performance of algorithms is compared and analyzed on two publicly available databases KAGGLE of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity.

Key-Words / Index Term
Deep Learning ,Convolutional Neural Networks, Transfer Learning, Automated Diabetic Retinopathy, Image Classification,Diabetes.

How to cite this article
Lakshmi Govind [1], Dharmendra Kumar [2] , “DIABETIC RETINOPATHY DETECTION USING TRANSFER LEARNING ”, Journal for Advance Research in Applied Sciences, 4, Issue-1, pp.463-471, Jun 2017. DPI:16.10089.JARAS.V4.I1.1756