Description
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
Table of Contents
1. Classification of ocular diseases using transfer learning approaches and glaucoma severity gradingD. Selvathi2. Early diagnosis of diabetic retinopathy using deep learning techniquesBam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scansN. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal4. Epidemiological surveillance of blindness using deep learning approachesKurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin5. Transfer learning-based detection of retina damage from optical coherence tomography imagesBam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural networkPoonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation techniqueT. Jemima Jebaseeli and D. Jasmine David8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classificationRanjitha Rajan and S.N. Kumar9. Deep learning approaches for the retinal vasculature segmentation in fundus imagesV. Sathananthavathi and G. Indumathi10. Grading of diabetic retinopathy using deep learning techniquesAsha Gnana Priya H, Anitha J and Ebenezer Daniel11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domainV.P. Ananthi and G. Santhiya12. U-net autoencoder architectures for retinal blood vessels segmentationS. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniquesAjantha Devi Vairamani
