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\u003cp\u003e\u003ci\u003eComputational Methods and Deep Learning for Ophthalmology\u003c/i\u003e 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. \u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e\u003cul\u003e \u003cli\u003ePresents the latest computational methods for designing and using Decision-Support Systems for ophthalmologic disorders in the human eye\u003c/li\u003e \u003cli\u003eConveys 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\u003c/li\u003e \u003cli\u003eExplains 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\u003c/li\u003e\u003c/ul\u003e |
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