A New Approach of Cup to Disk Ratio Based Glaucoma Detection Using Fundus Images

2016 ◽  
Vol 20 (1) ◽  
pp. 77-94 ◽  
Author(s):  
Muhammad Waseem Khan ◽  
Muhammad Sharif ◽  
Mussarat Yasmin ◽  
Steven Lawrence Fernandes
Keyword(s):  
Fundus Images ◽  
New Approach ◽  
Glaucoma Detection

ECNet: An evolutionary convolutional network for automated glaucoma detection using fundus images

2021 ◽  
Vol 67 ◽  
pp. 102559
Author(s):  
Deepak Ranjan Nayak ◽  
Dibyasundar Das ◽  
Banshidhar Majhi ◽  
Sulatha V. Bhandary ◽  
U. Rajendra Acharya
Keyword(s):  
Fundus Images ◽  
Convolutional Network ◽  
Glaucoma Detection

scholarly journals Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ozer Can Devecioglu ◽  
Junaid Malik ◽  
Turker Ince ◽  
Serkan Kiranyaz ◽  
Eray Atalay ◽  
...  
Keyword(s):  
Real Time ◽  
Fundus Images ◽  
Glaucoma Detection

Automatic Glaucoma Detection from Stereo Fundus Images

2020 ◽  
Author(s):  
Ee Ping Ong ◽  
Jun Cheng ◽  
Damon W.K. Wong ◽  
Elton L. T. Tay ◽  
Hwei Yee Teo ◽  
...  
Keyword(s):  
Fundus Images ◽  
Glaucoma Detection

scholarly journals Statistical Parameters for Glaucoma Detection from Color Fundus Images

2020 ◽  
Vol 171 ◽  
pp. 2675-2683
Author(s):  
Poonguzhali Elangovan ◽  
Malaya Kumar Nath ◽  
Madhusudan Mishra
Keyword(s):  
Fundus Images ◽  
Statistical Parameters ◽  
Glaucoma Detection ◽  
Color Fundus Images

scholarly journals FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

2021 ◽  
Author(s):  
M. Madhumalini ◽  
T. Meera Devi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Early Stage ◽  
Retinal Disease ◽  
Accurate Diagnosis ◽  
Fundus Images ◽  
Irreversible Loss ◽  
Glaucoma Detection ◽  
Gravitational Search

Abstract Glaucoma is a retinal disease that damages the eye's optic nerve, frequently causing an irreversible loss of vision. However, the accurate diagnosis of this disease is difficult but early-stage diagnosis may cure this retinal disease. The objective of this research is to diagnose glaucoma disease in the top of the eye's optical nerve. The proposed approach detects glaucoma via four major steps namely Data enhancement phase, segmentation phase, feature extraction phase, and classification phase by the fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) classifier. The proposed classifier is used for the exact classification of glaucoma infected images and normal images. Here, the proposed approach utilizes the statistical, textural, and vessel features from the segmented output. Also, the proposed FGSO algorithm is used for testing the deep neural network. From the experimental results, it is observed that the proposed glaucoma detection has obtained a sensitivity of 99.64%, a specificity of 97.84%, and an accuracy of 98.75% that outperforms other state-of-art methods.

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