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Few-shot Learning Using a Small-Sized Dataset of High-Resolution FUNDUS Images for Glaucoma Diagnosis

Mijung Kim, Jasper Zuallaert, Wesley De Neve
2017 Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care - MMHealth '17  
In this paper, we introduce a novel approach for early diagnosis of glaucoma in high-resolution FUNDUS images, only requiring a small number of training samples.  ...  Our experimental results show that our predictive model is able to obtain higher levels of effectiveness than vanilla deep convolutional neural networks.  ...  ACKNOWLEDGMENTS The research effort described in this paper was funded by Ghent University, the Ghent University Global Campus, imec, Flanders Innovation & Entrepreneurship (VLAIO), the Fund for Scientific  ... 
doi:10.1145/3132635.3132650 dblp:conf/mm/KimZN17 fatcat:2ssbx4bblffpncwvcvv2lem6ee

Diabetic Retinopathy, an Eye Disease Prediction System: Survey

Sheetal Mutha
2019 International Journal for Research in Applied Science and Engineering Technology  
The Digital Retinal Fundus image is analysed for the classification of various stages of Diabetic Retinopathy (DR).  ...  It's due to damage of the arteries and veins located in the fundus of the eye (retina) that are composed of light sensitive tissues.  ...  Present a new methodology for an automated diagnosis of glaucoma using digital fundus images based on Empirical Wavelet Transform (EWT).  ... 
doi:10.22214/ijraset.2019.1035 fatcat:k7w75y7vqbeepi6ji7xxomwbpm

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

Muhammad Mateen, Junhao Wen, Mehdi Hassan, Nasrullah Nasrullah, Song Sun, Shaukat Hayat
2020 IEEE Access  
INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology. 48784 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a timeconsuming and very expensive task, but in the automated system, artificial intelligence is  ...  In the literature, it was noted that most research work has been performed with the use of convolutional neural network models to develop deep multi-layer frameworks for the diagnosis of diabetic retinopathy  ... 
doi:10.1109/access.2020.2980055 fatcat:7gbkdxwyonb5bnao3yhs4us7sm

A Methodology for Glaucoma Disease Detection Using Deep Learning Techniques

Mian Usman Sattar, Fatima Ghani, Hamza Wazir Khan, Mehak Narmeen, Ahsan Mehmood
2022 International Journal of Computing and Digital Systems  
We have developed an architecture focused on the methodology of Deep Learning (DL), which is a Convolution Neural Network (CNN) for the classification of Glaucoma diseases.  ...  We used two different deep learning neural networks such as the Inception-V3 and the Vgg-16 Model for Glaucoma classification and identification purposes.  ...  Computer-aided diagnostics (CAD) is a non-invasive technique that uses digital fundus images to detect glaucoma in its early stage.  ... 
doi:10.12785/ijcds/110133 fatcat:4siv36rskbcctkbvvxxkwt6aiu

The impact of artificial intelligence in the diagnosis and management of glaucoma

Eileen L. Mayro, Mengyu Wang, Tobias Elze, Louis R. Pasquale
2019 Eye (London. 1987)  
Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform  ...  Autonomous DL algorithms are capable of maximizing information embedded in digital fundus photographs and ocular coherence tomographs to outperform ophthalmologists in disease detection.  ...  Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest.  ... 
doi:10.1038/s41433-019-0577-x pmid:31541215 pmcid:PMC7002653 fatcat:exejdukiv5gtpbouqbxs6kxlqi

Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs

Hanruo Liu, Liu Li, I. Michael Wormstone, Chunyan Qiao, Chun Zhang, Ping Liu, Shuning Li, Huaizhou Wang, Dapeng Mou, Ruiqi Pang, Diya Yang, Lai Jiang (+14 others)
2019 JAMA ophthalmology  
To establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations.  ...  In this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan  ...  Diagnosis with Convoluted Neural Networks.  ... 
doi:10.1001/jamaophthalmol.2019.3501 pmid:31513266 pmcid:PMC6743057 fatcat:5seww3m2fvbqnjt6eulowpadaq

Survey on Various Methods of Detecting Glaucoma

Swathi Anil, Prof. Elizabeth Issac
2018 International Journal of Trend in Scientific Research and Development  
In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis.  ...  Glaucoma detection using Deep Convolution Neural Network Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life.  ... 
doi:10.31142/ijtsrd10704 fatcat:kbyyswr545afzos2xj2itfatgq

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

Anna S. Mursch-Edlmayr, Wai Siene Ng, Alberto Diniz-Filho, David C. Sousa, Louis Arnold, Matthew B. Schlenker, Karla Duenas-Angeles, Pearse A. Keane, Jonathan G. Crowston, Hari Jayaram
2020 Translational Vision Science & Technology  
Nonsystematic literature review using the search combinations "Artificial Intelligence," "Deep Learning," "Machine Learning," "Neural Networks," "Bayesian Networks," "Glaucoma Diagnosis," and "Glaucoma  ...  ) test modalities used for the detection of glaucoma.  ...  used for testing) Convolutional neural network 98.1% accuracy 98% sensitivity 98% specificity Medeiros et al. (2019) 47 32,820 images from 1198 patients Deep learning convolutional neural  ... 
doi:10.1167/tvst.9.2.55 pmid:33117612 pmcid:PMC7571273 fatcat:gthfklo6pnd77dxfrjzrbggcn4

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

Munazza Tabassum, Tariq M. Khan, Muhammad Arslan, Syed S. Naqvi, Mansoor Ahmed, Hussain Ahmed Madni, Jawad Mirza
2020 IEEE Access  
INDEX TERMS Glaucoma diagnosis, OD and OC segmentation, deep convolutional neural network, semantic segmentation.  ...  Color fundus photography is used for examining the optic disc (OD) which is an important step in the diagnoses of glaucoma. This is done by estimating the cup-to-disc ratio (CDR).  ...  ACKNOWLEDGMENT The authors would like to thank the teams of DRISHTI-GS, RIM-ONE and REFUGE for maintaining and keeping these databases active and making them easily accessible for the researchers to carry  ... 
doi:10.1109/access.2020.2998635 fatcat:bhxcz5tqqvhgdfex4ewaqhv2b4

Development of a Deep-Learning-Based Artificial Intelligence Tool for Differential Diagnosis between Dry and Neovascular Age-Related Macular Degeneration

Tae-Young Heo, Kyoung Min Kim, Hyun Kyu Min, Sun Mi Gu, Jae Hyun Kim, Jaesuk Yun, Jung Kee Min
2020 Diagnostics  
Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus.  ...  Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images.  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/diagnostics10050261 pmid:32354098 fatcat:ostfnqnh5vhplgsj22tc6nqsfi

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  SPIE uses a seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume  ...  of nerve fiber layer defects on retinal fundus images using fully convolutional network for early diagnosis of glaucoma [10134-114] 10134 39 Inferring diagnosis and trajectory of wet age-related macular  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

Deep learning assisted detection of glaucomatous optic neuropathy and potential designs for a generalizable model

Yu-Chieh Ko, Shih-Yu Wey, Wei-Ta Chen, Yu-Fan Chang, Mei-Ju Chen, Shih-Hwa Chiou, Catherine Jui-Ling Liu, Chen-Yi Lee, Yuchen Qiu
2020 PLoS ONE  
Transfer learning based on VGGNet was used to construct a convolutional neural network (CNN) to identify GON.  ...  To evaluate ways to improve the generalizability of a deep learning algorithm for identifying glaucomatous optic neuropathy (GON) using a limited number of fundus photographs, as well as the key features  ...  The development of deep learning techniques in recent years, especially the use of convolutional neural network (CNN) and its variants for computer vision, has allowed improved medical image analysis through  ... 
doi:10.1371/journal.pone.0233079 pmid:32407355 fatcat:dcnzyr5ztvfqvb4mql4fymhelu

Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †

Mijung Kim, Jong Chul Han, Seung Hyup Hyun, Olivier Janssens, Sofie Van Hoecke, Changwon Kee, Wesley De Neve
2019 Applied Sciences  
In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques.  ...  Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively.  ...  Data augmentation: Overfitting occurs frequently when combining a small-sized dataset with a deep neural network, as was the case for the given set of fundus images and the architectures used, with the  ... 
doi:10.3390/app9153064 fatcat:56qpuyf5hffw7dhnxmi6fidesi

A Review on Glaucoma Disease Detection using Computerized Techniques

Faizan Abdullah, Rakhshanda Imtiaz, Hussain Ahmad Madni, Haroon Ahmed Khan, Tariq M. Khan, Mohammad A.U. Khan, Syed S. Naqvi
2021 IEEE Access  
This article aims to provide a comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images.  ...  INDEX TERMS Glaucoma, convolutional neural networks (CNN), diabetic retinopathy, cup-to-disc ratio (CDR), optic nerve head (ONH), optic cup (OC), optic disc (OD), intra ocular pressure (IOP).  ...  is performed on optic disc region, and MDCNN (multiple deep convolution neural networks) are used instead of the encoders.  ... 
doi:10.1109/access.2021.3061451 fatcat:lrg4fj4ixje2lilc3k7a33yl2u

Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

Muhammad Naseer Bajwa, Muhammad Imran Malik, Shoaib Ahmed Siddiqui, Andreas Dengel, Faisal Shafait, Wolfgang Neumeier, Sheraz Ahmed
2019 BMC Medical Informatics and Decision Making  
Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it  ...  With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology.  ...  We used Deep Convolutional Neural Network (DCNN) as shown in Fig. 2b on ODs extracted in stage one to classify the images into healthy and glaucoma affected images.  ... 
doi:10.1186/s12911-019-0842-8 pmid:31315618 pmcid:PMC6637616 fatcat:26bv3dy5qbfs5g73hektztq5di
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