A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images

Costa, Pedro; Galdrán, Adrián; Smailagic, Asim; Campilho, Aurélio

doi:10.1109/access.2018.2816003

Cited by 76 publications

(34 citation statements)

References 43 publications

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“…DR detection with retinal images used Machine Learning (ML) framework classification, which was lacked to define the level of DR identification [25]. The fundus images-based DR detection dealt with spatiotemporal images but took more time for execution [26]. Convolutional Neural Network (CNN) was used to classify the image based on the Random Forest (RF) classifier [27].…”

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks

Thomas¹,

Robinson²,

Julie³

et al. 2020

Preprint

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Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to diagnose and give a decision about the presence of retinopathy. The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy. The performance of the proposed model is compared with the related methods of DREAM, KNN, GD-CNN and SVM. Experimental results show that the proposed CNN performs better.

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Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks

Thomas¹,

Robinson²,

Julie³

et al. 2020

Preprint

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“…Finally, for the twofold separation referable versus non-referable, SVM is used. Another novelty is proposed by Pedro costa et.al [29] concerning BOVW method, in which encoding and classification stages are combined as single stage. K.S.Sreejini et al [30] proposed Probabilistic latent semantic analysis (pLSA) model which is one of the feature dimension reduction system reliant on topics instead of words.…”

Section: G Multiple Instance Learning (Mil) Framework -Bag Of Visualmentioning

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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“…Costa et al [9] focused on detection of diabetic retinopathy based on retinal images. They trained a model that correctly identifies DR depending on the occurrence of various retinal lesions.…”

Section: Literature Surveymentioning

confidence: 99%

Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods

Reddy

Sethi²,

Rajender³

2018

ICST Transactions on Scalable Information Systems

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Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that efficiently predicts diabetic retinopathy. Machine learning techniques like Decision tree, Random forest, Adaptive boosting and Bagging are used as primary algorithms to train predictive models. An algorithm namely 'Support Vector Machine using Gaussian kernel for retinopathy prediction' is proposed in this work. The proposed algorithm is compared with the primary algorithms based on five evaluation metrics namely accuracy, Youden's J index, concordance, Somers' D statistic and balanced accuracy. From the results obtained the proposed algorithm obtained better values for all considered evaluation metrics. Thus the use of SVM with Gaussian kernel is proposed to be used for prediction of diabetic retinopathy.

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A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images

Cited by 76 publications

References 43 publications

Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks

Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods

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