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 var...

Full description

Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Hemanth, D. Jude (Editor)
Format:	eBook
Language:	English
Published:	London ; San Diego, CA : Academic Press, an imprint of Elsevier, [2023]
Subjects:	Ophthalmology > Data processing. Deep learning (Machine learning) Eye > Diseases > Diagnosis > Data processing. Deep Learning Ophtalmologie > Informatique. Apprentissage profond. Œil > Maladies > Diagnostic > Informatique. Ophthalmology > Data processing Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.