Automated detection of exudates and macula for grading of diabetic macular edema

Akram, M. Usman; Tariq, Anam; Khan, Shoab A.; Javed, Mohammed

doi:10.1016/j.cmpb.2014.01.010

Cited by 96 publications

(47 citation statements)

References 17 publications

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“…The proposed system has three stages: the first stage is exudate segmentation based on the morphology method, the features from the segmented image are extracted in the second stage and classified into different severity levels using soft margin SVM in the last stage. In [9] have proposed another novel approach for detection and grading of macular edema to assist ophthalmologists in detecting the disease. An ensemble of Gaussian mixture model and SVM is presented as a new hybrid classifier for improved exudate detection even in the presence of other bright lesions.…”

Section: Related Workmentioning

confidence: 99%

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

Mukti¹,

Eswaran²,

Hashim³

et al. 2018

IJET

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In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features are applied to hierarchical support vector machine (SVM) classifier to obtain four types of grading levels, namely, normal, mild, moderate and severe. These grading levels are determined based on the number of anomalies such as microaneurysms, hard exudates and haemorrhages that are present in the fundus image. The performance of the proposed system is evaluated using fundus images from the Messidor database. Experiment results show that the proposed system can achieve an accuracy rate of 86.23%.

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Section: Related Workmentioning

confidence: 99%

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

Mukti¹,

Eswaran²,

Hashim³

et al. 2018

IJET

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show abstract

“…The accuracy obtained for the MESSIDOR database is 97%. Akram et al [8] presented an intelligent system for detecting ME using a Gaussian mixture model and detailed feature set. A hybrid classifier using the combination of Gaussian mixture model and the SVM classifiers is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Severity Analysis of Macular Edema using Random Tree Classifier

Prasad¹,

Vibha²,

Venugopal

2018

int. jour. eng. com. sci

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Abstract:Macular edema ensues when there is abnormal pile-up of fluid and results in swelling of the macula part of the retina. It is commonly associated with diabetes. It can be diagnosed by identifying exudates in the retinal images. In the proposed work, macular the retinal image is pre-processed, enhanced and segmented using morphological operations. The optic disc and macula are segmented. Various statistical features are extracted. Optimal features are selected using Haar wavelets. The selected features are classified using Random tree classifier to detect the severity of the disease in to three stages namely, normal, mild and critical. The accuracy obtained is 98.4%.

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“…The machine learning methods commonly used are neural networks [8,21], support vector machines (SVMs) [7,10], linear discriminant classifiers [12,20], the Naïve Bayes classifier [10], and the random forest algorithm [32]. A hybrid classifier as an ensemble of a Gaussian mixture model and an SVM was proposed in [1].…”

Section: Introductionmentioning

confidence: 99%