Computational Methods and Deep Learning for Ophthalmology

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 INDICE: 1. Review of online database and real time fundus/OCT image data for image analysis 2. Medical Image processing algorithms and filters for fundus image enhancement 3. Bioinspired optimization of parameters for retinal image analysis 4. Deep learning methods for feature extraction and analysis in OCT/Fundus images 5. Intelligent systems for early diagnosis of stages of vein occlusion diseases 6. Deep learning classifiers to differentiate stages of diabetic retinopathy 7. Fuzzy based segmentation of blood vessels and lesions in fundus images 8. Segmentation methods for anatomical structures in fundus images 9. Identification of glaucoma from fundus images using computational techniques 10. Segmentation of optic disc and analysis for the identification of abnormalities 11. Drusen detection in eye images using modified deep learning techniques 12. Computational methods for identification of macular degeneration 13. Exudates Detection and Macular Edema Estimation in eye Images 14. Classification of retinal diseases using transfer learning approaches 15. Deep learning-based identification of abnormalities using ERG signals 16. Efficient tool for screening of cataract using hybrid artificial neural networks

• ISBN: 978-0-323-95415-0
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 320
• Fecha Publicación: 01/03/2023
• Nº Volúmenes: 1
• Idioma: Inglés