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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xi 7. 1. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading - p. 1 8. 2. Early diagnosis of diabetic retinopathy using deep learning techniques - p. 17 9. 3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans - p. 35 10. 4. Epidemiological surveillance of blindness using deep learning approaches - p. 53 11. 5. Transfer learning-based detection of retina damage from optical coherence tomography images - p. 71 12. 6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network - p. 89 13. 7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique - p. 107 14. 8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images—an aid for segmentation and classification - p. 121 15. 9. Deep learning approaches for the retinal vasculature segmentation in fundus images - p. 139 16. 10. Grading of diabetic retinopathy using deep learning techniques - p. 157 17. 11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain - p. 175 18. 12. U-net autoencoder architectures for retinal blood vessels segmentation - p. 195 19. 13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques - p. 211 20. Index - p. 229
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xi 7. 1. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading - p. 1 8. 2. Early diagnosis of diabetic retinopathy using deep learning techniques - p. 17 9. 3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans - p. 35 10. 4. Epidemiological surveillance of blindness using deep learning approaches - p. 53 11. 5. Transfer learning-based detection of retina damage from optical coherence tomography images - p. 71 12. 6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network - p. 89 13. 7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique - p. 107 14. 8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images—an aid for segmentation and classification - p. 121 15. 9. Deep learning approaches for the retinal vasculature segmentation in fundus images - p. 139 16. 10. Grading of diabetic retinopathy using deep learning techniques - p. 157 17. 11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain - p. 175 18. 12. U-net autoencoder architectures for retinal blood vessels segmentation - p. 195 19. 13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques - p. 211 20. Index - p. 229
