Computational Methods and Deep Learning for Ophthalmology - Tapa blanda

Acerca del autor

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.

De la contraportada

Handbook of 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. Computer-aided decision support systems for various medical imaging modalities are available, and this is the first book concentrating specifically on application of these decision support systems to diseases related to the human eye. 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. Medical practitioners have difficulty accurately assessing and diagnosing ophthalmologic disorders of human beings. One significant feature of such eye diseases is the gradual nature of the progression of disease, which can be very difficult to detect. Clinicians need the support of biomedical engineering approaches for solving this problem. Dr. Hemanth and a distinguished team of contributing authors provide readers with leadingedge advances in computational approaches to assessment and diagnosis of ophthalmologic abnormalities.

The chapters in Handbook of Computational Methods and Deep Learning for Ophthalmology include coverage of computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. The 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 in the book include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders. The main feature that distinguishes this Handbook from other books is the in-depth details of the computational methods and the practical real-world scenarios in which they are applied. Numerous case studies are included which demonstrate the real-world application of decision support systems in assessment of the subjects.