Computational Methods and Deep Learning for Ophthalmology

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.