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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 retinopathyby 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.
Contained By:	Springer Nature eBook
Subject:	Diabetic retinopathyDiagnosis
Online resource:	https://doi.org/10.1007/978-981-19-3877-1
ISBN:	9789811938771$q(electronic bk.)

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$q(electronic bk.)

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

953598
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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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.
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