Computational Methods and Deep Learning for Ophthalmology

354 Pages·2023·17.917 MB·English

by  D. Jude D. Hemanth

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Computational Methods and Deep Learning for Ophthalmology 
Editor 
D.Jude Hemanth
Professor, Karunya University, Tamil Nadu, India
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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 
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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 

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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 
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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 
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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 
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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 

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Notices 
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