3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks

2021 ◽  
Author(s):  
Peipei Wang ◽  
Mingyuan Yuan ◽  
Yan He ◽  
Jiuai Sun
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fundus Images

scholarly journals Detection of retinal changes from illumination normalized fundus images using convolutional neural networks

2017 ◽  
Author(s):  
Kedir M. Adal ◽  
Peter G. van Etten ◽  
Jose P. Martinez ◽  
Kenneth Rouwen ◽  
Koenraad A. Vermeer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fundus Images

scholarly journals Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

2019 ◽  
Vol 85 ◽  
pp. 135-147
Author(s):  
Ričardas Toliušis ◽  
Olga Kurasova ◽  
Jolita Bernatavičienė
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Optical Disk ◽  
Fundus Images ◽  
Excellent Performance ◽  
Segmentation Algorithms ◽  
Performance Results

This article reviews the problems of eye bottom fundus analysis and semantic segmentation algorithms used to distinguish the eye vessels and the optical disk. Various diseases, such as glaucoma, hypertension, diabetic retinopathy, macular degeneration, etc., can be diagnosed through changes and anomalies of the vesssels and optical disk. Convolutional neural networks, especially the U-Net architecture, are well-suited for semantic segmentation. A number of U-Net modifications have been recently developed that deliver excellent performance results.

scholarly journals Evolving Convolutional Neural Networks for Glaucoma Diagnosis

2018 ◽  
Author(s):  
Alan Lima ◽  
Lucas B. Maia ◽  
Pedro Thiago Cutrim Dos Santos ◽  
Geraldo Braz Júnior ◽  
João D. S. De Almeida ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Optic Nerve ◽  
Visual Field ◽  
Convolutional Neural Networks ◽  
Ocular Disease ◽  
Fundus Images ◽  
Advanced Stage ◽  
Fundus Image ◽  
Glaucoma Diagnosis

Glaucoma is an ocular disease that causes damage to the eye's optic nerve and successive narrowing of the visual field in affected patients which can lead the patient, in advanced stage, to blindness. This work presents a study on the use of Convolutional Neural Networks (CNNs) for the automatic diagnosis through eye fundus images. However, building a perfect CNN involves a lot of effort that in many situations is not always able to achieve satisfactory results. The objective of this work is to use a Genetic Algorithm (GA) to optimize CNNs architectures through evolution that can helps in glaucoma diagnosis using eye's fundus image from RIM-ONE-r2 dataset. Our partial results demonstrate satisfactory results after training the best individual chosen by GA with the achievement of an accuracy of 91%.

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