Proceedings ArticleDOI
Improved Glaucoma Diagnosis Using Deep Learning
Saumya Borwankar,Raima Sen,Bhavin V. Kakani +2 more
- pp 1-4
TLDR
This work has automated the process of diagnosis of glaucoma using deep learning approaches and compared the results with previous approaches, which shows that this method has a better accuracy score.Abstract:
Glaucoma is termed as one of the top leading causes of vision loss and in many cases is irreversible [1]. It is a condition that damages the optic nerve and it goes unnoticed in early stages as the symptoms are not prominent in the early stages. Recent approaches have been made to automate the detection of glaucoma based on available datasets. World Health Organization also looks at eye defects to be critical as a result of the health evaluation conducted globally on health challenges. Survey points to the fact that it can become one of the primary concerns in 2020 which might affect around 75-80 million people. We have automated the process of diagnosis of glaucoma using deep learning approaches. Image processing has gained a lot of attraction and can be used for this problem in forming a computer-aided diagnosis for diseases. In the end, we have compared our results with previous approaches, which shows that our method has a better accuracy score.read more
Citations
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Posted ContentDOI
Energetic Glaucoma Segmentation and Classification Strategies using Depth Optimized Machine Learning Strategies
V. Elizabeth Jesi,Shabnam Mohamed Aslam,G. Ramkumar,A. Sabarivani,A. K. Gnanasekar,Prince Thomas +5 more
TL;DR: A new methodology is introduced to identify the Glaucoma on earlier stages called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results and the proposed approach assures the accuracy level of more than 96.2%.
Journal ArticleDOI
A Review of Deep Learning Techniques for Glaucoma Detection
Journal ArticleDOI
Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
Mohammad Jameel Zedan,Mohd Asyraf Zulkifley,Ahmad Asrul Ibrahim,Asraf Mohamed Moubark,Nor Azwan Mohamed Kamari,Siti Raihanah Abdani +5 more
TL;DR: In this article , a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma is provided, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Causes of vision loss worldwide, 1990–2010: a systematic analysis
Rupert R A Bourne,Gretchen A Stevens,Richard A. White,Jennifer L. Smith,Seth Flaxman,Holly Price,Jost B. Jonas,Jill E Keeffe,Janet L Leasher,Kovin Naidoo,Kovin Naidoo,Konrad Pesudovs,Serge Resnikoff,Hugh R. Taylor +13 more
TL;DR: The differences and temporal changes found in causes of blindness and MSVI have implications for planning and resource allocation in eye care.
Journal ArticleDOI
Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.
TL;DR: A deep learning system can detect referable GON with high sensitivity and specificity and coexistence of high or pathologic myopia is the most common cause resulting in false-negative results.
Proceedings ArticleDOI
Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation
TL;DR: A comprehensive dataset of retinal images which include both normal and glaucomatous eyes and manual segmentations from multiple human experts is presented and area and boundary-based evaluation measures are presented to evaluate a method on various aspects relevant to the problem ofglaucoma assessment.
Proceedings ArticleDOI
Glaucoma detection based on deep convolutional neural network
TL;DR: A deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis and results show area under curve (AUC) of the receiver operating characteristic curve in glau coma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms.