Retinal (Glaucoma) Image Synthesis | Using Traditional and Advanced Data Augmentation Techniques

Sravani Devi Yerrarapu

ISBN 10: 6206737381 / ISBN 13: 9786206737384

Published by LAP LAMBERT Academic Publishing, 2023

About this Item

Retinal (Glaucoma) Image Synthesis | Using Traditional and Advanced Data Augmentation Techniques | Sravani Devi Yerrarapu | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206737384 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 127329315

Bibliographic Details

• Title: Retinal (Glaucoma) Image Synthesis | Using Traditional and Advanced Data Augmentation Techniques
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 6206737381
• ISBN 13: 9786206737384
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 107 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The human eye is an organ that reacts to light and pressure. Many diseases, disorders, and age-related changes may affect the eyes and surrounding structures. One of the eye diseases is glaucoma. Glaucoma is a condition in which fluid pressure rises within the eye. Without treatment, it can damage the optic nerve and lead to vision loss. The early detection of glaucoma minimizes the risk of vision loss. The proposed model synthesizes highly realistic controllable fundus images to obtain precision in detecting glaucoma through a deep learning model.A generative adversarial network (GAN) is an unsupervised machine learning technique that can be used to augment datasets and yield collected images to be indistinguishable from real-world data. The Deep Convolutional GAN (DCGAN), another variant of GAN, suggests the architectural constraints on the model required to develop high-quality generator models effectively. The enhanced dataset, obtained from data augmentation and the original ACRIMA dataset of fundus images, are separately given to the CNN classification model for detecting glaucoma disease.