Deep convolutional neural network for the prognosis of diabetic retinopathy / A. Shanthini, Gunasekaran Manogaran, G. Vadivu
Shanthini, A.; Manogaran, Gunasekaran, 1989-; Vadivu, G.
2023
RE661.D5
Available Online

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Title Deep convolutional neural network for the prognosis of diabetic retinopathy / A. Shanthini, Gunasekaran Manogaran, G. Vadivu
Author Shanthini, A.
ISBN 9789811938771 (electronic bk.)
9811938776 (electronic bk.)
9789811938764 (print)
9811938768
DOI https://doi.org/10.1007/978-981-19-3877-1
Publication Details Singapore : Springer, [2023]
Language English
Description 1 online resource (80 pages)
Item Number 10.1007/978-981-19-3877-1 doi
Call Number RE661.D5
Dewey Decimal Classification 617.7/35
Summary This book discusses a detailed overview of diabetic retinopathy, symptoms, causes, and screening methodologies. Using a deep convolution neural network and visualizations techniques, this work develops a prognosis system used to automatically detect the diabetic retinopathy disease from captured retina images and help improve the prediction rate of diagnosis. This book gives the readers an understanding of the diabetic retinopathy disease and recognition process that helps to improve the clinical analysis efficiency. It caters to general ophthalmologists and optometrists, diabetologists, and internists who encounter diabetic patients and most prevalent retinal diseases daily.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed September 2, 2022).
Added Author Manogaran, Gunasekaran, 1989-
Vadivu, G.
Series Series in bioengineering.
Available in Other Form Deep Convolutional Neural Network for the Prognosis of Diabetic Retinopathy.
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Introduction
Chapter 1
Background of diabetic retinopathy
Chapter 2
Classification of diabetic retinopathy
Chapter 3
Deep convolutional neural network architecture
Chapter 4
Deep convolutional neural network applications and visualization tools
Chapter 5
Multi-platform deployment for prognosis system
Chapter 6
Case Studies for diabetic retinopathy with a deep learning system.

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