Computational Methods and Deep Learning for Ophthalmology
- 1st Edition - February 18, 2023
- Imprint: Academic Press
- Editor: D. Jude Hemanth
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 1 5 - 0
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 1 4 - 3
Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems f… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteComputational 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
Researchers, developers, and industry professionals in Machine Learning, Deep Learning, Computational Intelligence, Medical Image Analysis, and Medical Decision Support Systems, as well as researchers and industry professionals in biomedical imaging, and human-machine interaction. In addition, clinicians and ophthalmologists who are involved in rese
- Cover image
- Title page
- Table of Contents
- 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
- 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
- 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
- 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
- 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
- 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
- Edition: 1
- Published: February 18, 2023
- No. of pages (Paperback): 250
- No. of pages (eBook): 250
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323954150
- eBook ISBN: 9780323954143
DH
D. Jude Hemanth
Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of “Visiting Professor” in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the “Research Scientist” of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain.
Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.
Affiliations and expertise
Professor, ECE Department, Karunya Institute of Technology and Sciences, Coimbatore, IndiaRead Computational Methods and Deep Learning for Ophthalmology on ScienceDirect