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Deep convolutional neural network fo...

Shanthini, A.

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Deep convolutional neural network for the prognosis of diabetic retinopathy

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep convolutional neural network for the prognosis of diabetic retinopathy/ by A. Shanthini, Gunasekaran Manogaran, G. Vadivu.
Author:	Shanthini, A.
other author:	Manogaran, Gunasekaran.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	ix, 75 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Diabetic retinopathy - Diagnosis -
Online resource:	https://doi.org/10.1007/978-981-19-3877-1
ISBN:	9789811938771

Deep convolutional neural network for the prognosis of diabetic retinopathy
Shanthini, A.

Deep convolutional neural network for the prognosis of diabetic retinopathy[electronic resource] /by A. Shanthini, Gunasekaran Manogaran, G. Vadivu. - Singapore :Springer Nature Singapore :2023. - ix, 75 p. :ill., digital ;24 cm. - Series in bioengineering,2196-887X. - Series in bioengineering..

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.

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.

ISBN: 9789811938771

Standard No.: 10.1007/978-981-19-3877-1doiSubjects--Topical Terms:

3625760
Diabetic retinopathy
--Diagnosis

LC Class. No.: RE661.D5

Dewey Class. No.: 617.735

Deep convolutional neural network for the prognosis of diabetic retinopathy
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245 1 0 $a Deep convolutional neural network for the prognosis of diabetic retinopathy $h [electronic resource] / $c by A. Shanthini, Gunasekaran Manogaran, G. Vadivu.
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300 $a ix, 75 p. : $b ill., digital ; $c 24 cm.
490 1 $a Series in bioengineering, $x 2196-887X
505 0 $a 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.
520 $a 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.
650 0 $a Diabetic retinopathy $x Diagnosis $x Technological innovations. $3 3625760
650 0 $a Neural networks (Computer science) $3 532070
650 1 4 $a Biomedical Engineering and Bioengineering. $3 3381533
650 2 4 $a Ophthalmology. $3 862704
650 2 4 $a Signal, Speech and Image Processing. $3 3592727
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Manogaran, Gunasekaran. $3 3592739
700 1 $a Vadivu, G. $3 3599095
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