An Enhanced Approach for Automated Glaucoma Diagnosis in Retinal Fundus Images

2021 ◽  
pp. 185-197
Author(s):  
Osama M. Kamara ◽  
Ahmed H. Asad ◽  
Hesham A. Hefny
Keyword(s):  
Fundus Images ◽  
Glaucoma Diagnosis ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diagnose Glaucoma in Retinal fundus Images

2017 ◽  
Author(s):  
Javedkhan Y. Pathan ◽  
Dr.Pramod Patil
Keyword(s):  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

scholarly journals Modeling and Enhancing Low-Quality Retinal Fundus Images

2020 ◽  
pp. 1-1
Author(s):  
Ziyi Shen ◽  
Huazhu Fu ◽  
Jianbing Shen ◽  
Ling Shao
Keyword(s):  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3922
Author(s):  
Sheeba Lal ◽  
Saeed Ur Rehman ◽  
Jamal Hussain Shah ◽  
Talha Meraj ◽  
Hafiz Tayyab Rauf ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Feature Fusion ◽  
Speckle Noise ◽  
Fundus Images ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Adversarial Attack ◽  
Retinal Fundus Images ◽  
Attacks And Defenses ◽  
Retinal Fundus

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

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