Image processing and classification in diabetic retinopathy: A review

Ahmad, Arslan; Mansoor, Asif; Mumtaz, Rafia; Khan, Mukaram; Mirza, Salma

doi:10.1109/euvip.2014.7018362

Cited by 34 publications

(12 citation statements)

References 39 publications

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“…Generally, in the manual diagnosis, the specialists will concentrate on lesions like microaneurysms, exudates, and hemorrhages in examining the fundus pictures. Hence, the majority of the researchers concentrated on automatically identify and categorize the lesions [19]. In the paper [20], the author's Shahin et al built up a framework to automatically categorize retinal fundus pictures as having and not having 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.

179

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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.

179

View full text Add to dashboard Cite

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“…The proposed hybrid classification technique is compared with existing methods [6] [15] [17]. The obtained result is checked for accuracy which provides best result (almost 100%) when compared with existing approaches.…”

Section: Accuracymentioning

confidence: 99%

Multistage Classification of Diabetic Retinopathy Using Fuzzyneural Network Classifier

Prasad¹,

Vibha²,

Venugopal³

2018

IJIVP

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Diabetic Retinopathy (DR) is complicated disorder in human retina which is affected due to an increasing amount of insulin in blood that results in vision impairment. Early detection of DR is used to support the patients to prevent blindness and to be aware of this disease. This paper proposes a novel technique for detecting DR using hybrid classifiers. It includes pre-processing of the image, segmentation of region of interest, feature extraction and classification. Retinal structures like microaneurysms, exudates, hemorrhages and blood vessels are segmented. Classification is performed with integration of Fuzzy logical System and Neural Network (NN) which improves the accuracy of classification. Experimentation is carried out with the MESSIDOR data set. Results are compared against various performance metrics like accuracy, sensitivity and specificity. An accuracy close to 100 percent and low average error rate of 0.012 are obtained using the proposed method. The results obtained are encouraging.

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“…Since human experts usually focus on some typical lesions associated with DR such as microaneurysms, hemorrhages and hard exudates (see Fig. 1) when evaluating fundus photographs, many works paid attention to automatedly detect and segment these lesions or calculate some numerical indexes [3]. Shahin et al [4] developed a system to automatedly classify retinal fundus images into those with or without proliferate diabetes retinopathy.…”

Section: Introductionmentioning

confidence: 99%

Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network

et al. 2019

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Diabetic retinopathy (DR) is an important cause of blindness worldwide. However, DR is hard to be detected in the early stages, and the diagnostic procedure can be time-consuming even for the experienced experts. Therefore, a computer-aided diagnosis method based on deep learning algorithms is proposed to automatedly diagnose the referable diabetic retinopathy by classifying color retinal fundus photographs into two grades. In this paper, a novel convolutional neural network model with the Siamese-like architecture is trained with a transfer learning technique. Different from the previous works, the proposed model accepts binocular fundus images as inputs and learns their correlation to help to make a prediction. In the case with a training set of only 28 104 images and a test set of 7024 images, an area under the receiver operating curve of 0.951 is obtained by the proposed binocular model, which is 0.011 higher than that obtained by the existing monocular model. To further verify the effectiveness of the binocular design, a binocular model for five-class DR detection is also trained and evaluated on a 10% validation set. The result shows that it achieves a kappa score of 0.829 which is higher than that of the existing non-ensemble model.INDEX TERMS Biomedical imaging processing, diabetic retinopathy, fundus photograph, convolutional neural network, deep learning, Siamese-like network.

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Image processing and classification in diabetic retinopathy: A review

Cited by 34 publications

References 39 publications

Diabetic retinopathy detection and classification using capsule networks

Diabetic retinopathy detection and classification using capsule networks

Multistage Classification of Diabetic Retinopathy Using Fuzzyneural Network Classifier

Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network

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