Ophthalmology
Computational Methods and Deep Learning for Ophthalmology 2023
Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye. Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders.
This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration.
Presents the latest computational methods for designing and using Decision-Support Systems for ophthalmologic disorders in the human eye Conveys the role of a variety of computational methods and algorithms for efficient and effective diagnosis of ophthalmologic disorders, including Diabetic Retinopathy, Glaucoma, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders Explains how to develop and apply a variety of computational diagnosis systems and technologies, including medical image processing algorithms, bioinspired optimization, Deep Learning, computational intelligence systems, fuzzy-based segmentation methods, transfer learning approaches, and hybrid Artificial Neural Networks
Table of contents :
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 approaches
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 coherence
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 segmentation
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 fundus
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
