On the Relevance of Very Deep Networks for Diabetic Retinopathy Diagnostics

Akilesh, B and Marwah, Tanya and Balasubramanian, Vineeth N and Kumar, Rajamani (2017) On the Relevance of Very Deep Networks for Diabetic Retinopathy Diagnostics. In: Applications of Cognitive Computing Systems and IBM Watson. Springer, Singapore, pp. 47-54. ISBN 9789811064180

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Abstract

Detection of Diabetic Retinopathy (DR) has been worked on for a long time, but no commercially viable solutions that work for different populations exist yet. In this work, we investigate the performance of Very Deep Networks for the binary classification of fundus images provided by EyePACS as part of Kaggle’s DR detection challenge.

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IITH Creators:	IITH Creators ORCiD Balasubramanian, Vineeth N UNSPECIFIED
Item Type:	Book Section
Uncontrolled Keywords:	Diabetic retinopathy detection Deep learning Convolutional neural networks ResNet Gradient noise regularization
Subjects:	Computer science
Divisions:	Department of Computer Science & Engineering
Depositing User:	Team Library
Date Deposited:	28 May 2018 10:51
Last Modified:	28 May 2018 10:51
URI:	http://raiithold.iith.ac.in/id/eprint/3967
Publisher URL:	http://doi.org/10.1007/978-981-10-6418-0_6
Related URLs:	Google Scholar

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