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…mehr
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. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading D. Selvathi 2. Early diagnosis of diabetic retinopathy using deep learning techniques Bam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan 3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans N. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal 4. Epidemiological surveillance of blindness using deep learning approaches Kurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin 5. Transfer learning-based detection of retina damage from optical coherence tomography images Bam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan 6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network Poonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath 7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique T. Jemima Jebaseeli and D. Jasmine David 8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classification Ranjitha Rajan and S.N. Kumar 9. Deep learning approaches for the retinal vasculature segmentation in fundus images V. Sathananthavathi and G. Indumathi 10. Grading of diabetic retinopathy using deep learning techniques Asha Gnana Priya H, Anitha J and Ebenezer Daniel 11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain V.P. Ananthi and G. Santhiya 12. U-net autoencoder architectures for retinal blood vessels segmentation S. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao 13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques Ajantha Devi Vairamani
1. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading D. Selvathi 2. Early diagnosis of diabetic retinopathy using deep learning techniques Bam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan 3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans N. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal 4. Epidemiological surveillance of blindness using deep learning approaches Kurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin 5. Transfer learning-based detection of retina damage from optical coherence tomography images Bam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan 6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network Poonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath 7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique T. Jemima Jebaseeli and D. Jasmine David 8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classification Ranjitha Rajan and S.N. Kumar 9. Deep learning approaches for the retinal vasculature segmentation in fundus images V. Sathananthavathi and G. Indumathi 10. Grading of diabetic retinopathy using deep learning techniques Asha Gnana Priya H, Anitha J and Ebenezer Daniel 11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain V.P. Ananthi and G. Santhiya 12. U-net autoencoder architectures for retinal blood vessels segmentation S. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao 13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques Ajantha Devi Vairamani
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