A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis

Gegúndez-Arias, Manuel Emilio; Marín, D.; Ponte, Beatriz; Álvarez, F. Rodríguez; Garrido, Javier; Ortega, Carlos; Vasallo, Manuel Jesús; Bravo, José Manuel

doi:10.1016/j.compbiomed.2017.07.007

Cited by 40 publications

(15 citation statements)

References 29 publications

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“…At last, the advancement of the considered methodology was demonstrated over different models. The researchers of the article [36] have built up a strategy, to identify the presence of hemorrhages and microaneurysms based on supervised classification models. The authors have assessed different patients who were suffering from diabetic retinopathy.…”

Section: Review Of Literaturementioning

confidence: 99%

Diabetic retinopathy detection and classification using capsule networks

Kalyani

Janakiramaiah

Karuna

et al. 2021

Complex Intell. Syst.

160

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Nowadays, diabetic retinopathy is a prominent reason for blindness among the people who suffer from diabetes. Early and timely detection of this problem is critical for a good prognosis. An automated system for this purpose contains several phases like identification and classification of lesions in fundus images. Machine learning techniques based on manual extraction of features and automatic extraction of features with convolution neural network have been presented for diabetic retinopathy detection. The recent developments like capsule networks in deep learning and their significant success over traditional machine learning methods for a variety of applications inspired the researchers to apply them for diabetic retinopathy diagnosis. In this paper, a reformed capsule network is developed for the detection and classification of diabetic retinopathy. Using the convolution and primary capsule layer, the features are extracted from the fundus images and then using the class capsule layer and softmax layer the probability that the image belongs to a specific class is estimated. The efficiency of the proposed reformed network is validated concerning four performance measures by considering the Messidor dataset. The constructed capsule network attains an accuracy of 97.98%, 97.65%, 97.65%, and 98.64% on the healthy retina, stage 1, stage 2, and stage 3 fundus images.

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Section: Review Of Literaturementioning

confidence: 99%

Diabetic retinopathy detection and classification using capsule networks

Kalyani

Janakiramaiah

Karuna

et al. 2021

Complex Intell. Syst.

160

View full text Add to dashboard Cite

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“…Messidor retains the total number of 1200 retinal fundus photographs. Gegundez-Arias et al [44] applied the Messidor dataset to screen out the DR.…”

Section: ) Public Datasetsmentioning

confidence: 99%

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

et al. 2020

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Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. 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 used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology.

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“…The Random Forest classifier is utilized to separate among extruders and different structures like vessel fragments. The course of action of images as DR and no-DR has been done by M. Gegundez-Arias et al [18] by recognizing the MA and HEM. In the primary stage identification of the MA and HEM applicants is performed.…”

Section: Red Lesion Detectionmentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis

Cited by 40 publications

References 29 publications

Diabetic retinopathy detection and classification using capsule networks

Diabetic retinopathy detection and classification using capsule networks

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

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

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