Computational Methods and Deep Learning for Ophthalmology PDF
Preview Computational Methods and Deep Learning for Ophthalmology
1 Computational Methods and Deep Learning for Ophthalmology Editor D.Jude Hemanth Professor, Karunya University, Tamil Nadu, India 2 Table of Contents Cover image Title page Copyright Contributors 1. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading 1.1. Introduction 1.2. Literature review 1.3. Proposed methodology 1.4. Results and discussion 1.5. Conclusion 2.Early diagnosis of diabetic retinopathy using deep learning techniques 2.1. Introduction 2.2. Related background 2.3. Experimental methodology 2.4. Proposed flow 2.5. Results and discussion 3 2.6. Conclusion and future direction 3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans 3.1. Introduction 3.2. Structural changes of DME 3.3. Convolutional neural networks 3.4. Results and discussion 3.5. Conclusion 4. Epidemiological surveillance of blindness using deep learning ap- proaches 4.1. Conceptualizing surveillance systems in ophthalmic epidemiology 4.2. Deep learning in ophthalmic epidemiological surveillance 4.3. Limitations 4.4. Conclusion 5. Transfer learning-based detection of retina damage from optical coher- ence tomography images 5.1. Introduction 5.2. Experimental methodology 5.3. Proposed model 4 5.4. Experimental results and observations 5.5. Conclusion 6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network 6.1. Introduction 6.2. Related work 6.3. Methodology 6.4. Experimental findings 6.5. Conclusion 7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN seg- mentation technique 7.1. Introduction 7.2. Literature review 7.3. Materials 7.4. Methodology 7.5. Experimental analysis 7.6. Discussions 7.7. Conclusion 5 8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images—an aid for segmentation and classification 8.1. Introduction 8.2. Methodology 8.3. Results and discussion 8.4. Conclusion 9. Deep learning approaches for the retinal vasculature segmentation in fun- dus images 9.1. Introduction 9.2. Significance of deep learning 9.3. Convolutional neural network 9.4. Fully convolved neural network 9.5. Retinal blood vessel extraction 9.6. Artery/vein classification 9.7. Summary 10. Grading of diabetic retinopathy using deep learning techniques 10.1. Introduction 10.2. Materials and methods 10.3. Methodology 6 10.4. Results and discussion 10.5. Conclusion 11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain 11.1. Introduction 11.2. Preliminary ideas 11.3. Proposed method of blood vessel extraction 11.4. Proposed method of lesion extraction 11.5. Experimental analysis 11.6. Conclusion 12. U-net autoencoder architectures for retinal blood vessels segmentation 12.1. Introduction 12.2. Related works 12.3. Proposed works 12.4. Experiment 12.5. Conclusion 13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques 13.1. Introduction 7 13.2. Fundus image analysis 13.3. Eye diseases with retinal manifestation 13.4. Diagnosis of glaucoma 13.5. Diagnosis of diabetic retinopathy 13.6. Conclusion Index 8 Copyright Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United King- dom Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writ- ing from the publisher. Details on how to seek permission, further infor- mation about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copy- right Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own expe- rience and knowledge in evaluating and using any information, meth- ods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a pro- fessional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any 9 methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-323-95415-0 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Mara Conner Acquisitions Editor: Chris Katsaropoulos Editorial Project Manager: Tom Mearns Production Project Manager: Prem Kumar Kaliamoorthi Cover Designer: Christian Bilbow Typeset by TNQ Technologies 10
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