Computational Methods and Deep Learning for Ophthalmology

Häftad, Engelska, 2023

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Beskrivning

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

Produktinformation

Utgivningsdatum:2023-02-24
Mått:191 x 235 x 21 mm
Vikt:720 g
Format:Häftad
Språk:Engelska
Antal sidor:250
Förlag:Elsevier Science
ISBN:9780323954150

Utforska kategorier

Hälsoinformatik inom Medicin
Systemvetenskap och AI inom Data och IT
Biovetenskap inom Naturvetenskap och teknik

Mer om författaren

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.

Innehållsförteckning

1. Classification of ocular diseases using transfer learning approaches and glaucoma severity gradingD. Selvathi2. Early diagnosis of diabetic retinopathy using deep learning techniquesBam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scansN. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal4. Epidemiological surveillance of blindness using deep learning approachesKurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin5. Transfer learning-based detection of retina damage from optical coherence tomography imagesBam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural networkPoonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation techniqueT. Jemima Jebaseeli and D. Jasmine David8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classificationRanjitha Rajan and S.N. Kumar9. Deep learning approaches for the retinal vasculature segmentation in fundus imagesV. Sathananthavathi and G. Indumathi10. Grading of diabetic retinopathy using deep learning techniquesAsha Gnana Priya H, Anitha J and Ebenezer Daniel11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domainV.P. Ananthi and G. Santhiya12. U-net autoencoder architectures for retinal blood vessels segmentationS. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniquesAjantha Devi Vairamani