Computational Methods and Deep Learning for Ophthalmology

Computational Methods and Deep Learning for Ophthalmology is a comprehensive guide that explores the latest concepts and techniques for designing and using advanced computer-aided diagnosis systems to detect ophthalmologic abnormalities in the human eye. The book covers a range of computational approaches, including Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks, for diagnosing and assessing a variety of ophthalmologic abnormalities such as Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders.

This handbook serves as a significant resource for biomedical engineers, computer scientists, and multidisciplinary researchers seeking to address the increasing prevalence of diseases like Diabetic Retinopathy, Glaucoma, and Macular Degeneration. It provides detailed explanations on 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. The book also emphasizes the importance of efficient and effective diagnosis of ophthalmologic disorders, enabling readers to better understand the role of computational methods and algorithms in the field.

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