Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Barros, Daniele Montenegro da Silva; Moura, Julio C. C.; Freire, Cefas Rodrigues; Taleb, Alexandre Chater; Valentim, Ricardo Alexsandro de Medeiros; Morais, Philippi Sedir Grilo de

doi:10.1186/s12938-020-00767-2

Cited by 79 publications

(52 citation statements)

References 71 publications

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“…Other studies, [ 20 , 108 ], followed the systematic framework in their reviews: [ 20 ] discussed the main algorithms used for glaucoma detection using ML, indicating the importance of this technology from a medical aspect, especially retinal image processing, whereas [ 108 ] performed a systematic review on investigating and evaluating DL methods’ performance for automatically detecting glaucoma using fundus images.…”

Section: Resultsmentioning

confidence: 99%

“…ONH assessment is a widely used glaucoma screening tool that utilizes differential division to distinguish between glaucomatous and normal images [ 17 ]. Manual calculations of ONH geometric structures, such as the cup-to-disc ratio (CDR); inferior, superior, nasal, and temporal (ISNT) rule; disc diameter; and rim area, are recommended as diagnostic features for glaucoma screening [ 18 - 20 ]. Among them, the CDR is a reliable therapeutic feature for early glaucoma screening and diagnosis [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…To date, several review articles summarizing the technology of DL in ophthalmology have been published [ 20 , 52 - 55 ]. Nevertheless, none of them have particularly focused on the emerging breakthrough GAN techniques using fundus photographs.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Saeed¹,

Abdullah²,

Che-Hamzah³

et al. 2021

J Med Internet Res

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Background Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs). Objective This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology. Methods To organize this review comprehensively, articles and reviews were collected using the following keywords: (“Glaucoma,” “optic disc,” “blood vessels”) and (“receptive field,” “loss function,” “GAN,” “Generative Adversarial Network,” “Deep learning,” “CNN,” “convolutional neural network” OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020. Results We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease. Conclusions Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Saeed¹,

Abdullah²,

Che-Hamzah³

et al. 2021

J Med Internet Res

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“…Finally Barros et al [1] perform a deep analysis on all the machine learning algorithms and CNNs applied to glaucoma detection using many datasets. In Table 1 we show in an arbitrary order a comparison amongst the results obtained from all previously mentioned works, which were obtained according to the dataset each work used.…”

Section: Related Workmentioning

confidence: 99%

Optic Disc Preprocessing for Reliable Glaucoma Detection in Small Datasets

2021

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Glaucoma detection is an important task, as this disease can affect the optic nerve, and this could lead to blindness. This can be prevented with early diagnosis, periodic controls, and treatment so that it can be stopped and prevent visual loss. Usually, the detection of glaucoma is carried out through various examinations such as tonometry, gonioscopy, pachymetry, etc. In this work, we carry out this detection by using images obtained through retinal cameras, in which we can observe the state of the optic nerve. This work addresses an accurate diagnostic methodology based on Convolutional Neural Networks (CNNs) to classify these optical images. Most works require a large number of images to train their CNN architectures, and most of them use the whole image to perform the classification. We will use a small dataset containing 366 examples to train the proposed CNN architecture and we will only focus on the analysis of the optic disc by extracting it from the full image, as this is the element that provides the most information about glaucoma. We experiment with different RGB channels and their combinations from the optic disc, and additionally, we extract depth information. We obtain accuracy values of 0.945, by using the GB and the full RGB combination, and 0.934 for the grayscale transformation. Depth information did not help, as it limited the best accuracy value to 0.934.

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“…Researchers develop a machine learning predictive model that can select the five data features of the patients 1) visual field test,2) a retinal nerve fiber layer optical coherence tomography (RNFL OCT) test, 3) a general examination with 4) an intraocular pressure (IOP) measurement and 5) fundus photography. Finally, they used support vector machine (SVM), C5.0, random forest (RF), and XGboost algorithmsto test the predicted model [1].The researchers developed different prediction models based on deep learning techniques and use image data for prediction [2][3][4][5][6]. Also the traditional machine learning models areused for glaucoma prediction [7][8][9].Researchers comprehensively reviewed in their different articles about glaucoma, its types, cause, effect, and possible treatments.…”

Section: Related Workmentioning

confidence: 99%

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

Bandyopadhyay¹,

Bose²,

Dutta³

et al. 2021

Preprint

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In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

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Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Cited by 79 publications

References 71 publications

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Optic Disc Preprocessing for Reliable Glaucoma Detection in Small Datasets

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

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