Automatic classification of diabetic macular edema in digital fundus images

Lim, Sangsoon; Zaki, Wan Mimi Diyana Wan; Hussain, Aini; Lim, Sin Liang; Kusalavan, S.

doi:10.1109/chuser.2011.6163730

Cited by 56 publications

(30 citation statements)

References 7 publications

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“…This has led to improved results for prediction but may be improved further. Our proposed system demonstrates comparable results with other methods in the literature; we obtain higher accuracy than [21] who achieved 85.2% and similar accuracy to machine learning methods such as [7] and [10] which had 94.1% and 95.56% respectively. Although our accuracy is lower than these two models, we achieve this without the need for prior feature extraction, exudate and macula segmentation or the removal of retinal blood vessels.…”

Section: Resultssupporting

confidence: 71%

Diabetic Macular Edema Grading Based on Deep Neural Networks

Al-Bander

Al-Nuaimy

Al-Taee

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the severity of DME using color retinal fundus images. An automated DME diagnosis system based on the proposed featurelearning approach is developed to help early diagnosis of the disease and thus averts (or delays) its progression. It utilizes the convolutional neural networks (CNNs) to identify and extract features of DME automatically without any kind of user intervention. The developed prototype was trained and assessed by using an existing MESSIDOR dataset of 1200 images. The obtained preliminary results showed accuracy of (88.8 %), sensitivity (74.7%) and specificity (96.5 %). These results compare favorably to state-of-the-art findings with the added benefit of an automatic feature-learning approach rather than a time-consuming handcrafted approach.

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Section: Resultssupporting

confidence: 71%

Diabetic Macular Edema Grading Based on Deep Neural Networks

Al-Bander

Al-Nuaimy

Al-Taee

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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“…Watershed segmentation based DME severity classification using color fundus images is described in [7]. Green component of image is extracted and optic disc is eliminated after preprocessing steps such as CLAHE (Contrast limited adaptive histogram equalization) and contrast stretching.…”

Section: Related Workmentioning

confidence: 99%

“…Since the optic disc has similar features with exudates [7,10], optic disc localization and removal is necessary prior to exudates segmentation/ classification. Optic disc localization is an important step for segmentation of other retinal features such as fovea and macula [5,7].…”

Section: Related Workmentioning

confidence: 99%

“…Optic disc localization is an important step for segmentation of other retinal features such as fovea and macula [5,7]. Segmentation of optic disc is performed in [14] employing morphological and edge detection techniques and Hough transform.…”

Section: Related Workmentioning

confidence: 99%

“…Direct methods include stereoscopy or optical computed tomography [6]. Indirect methods, detect the presence of exudates in the retina [7]. DME has no early signs.…”

Section: Introductionmentioning

confidence: 99%

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Severity Grading of DME from Retina Images: A Combination of PSO and FCM with Bayes Classifier

S¹,

Govindan²

2013

IJCA

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Diabetic macular edema (DME) is the main cause of visual impairments in patients with diabetic retinopathy and leads to vision loss if left untreated. In this paper, an automatic approach for severity grading of DME is introduced. The approach involves preprocessing, combination of Particle Swarm Optimization (PSO) algorithm and Fuzzy C-Means Clustering for exudates segmentation, optic disc elimination, fovea and macular region localization, and classification. The Bayes classifier separates the lesions to exudates and nonexudates. The severity of the disease is graded into categories such as normal, grade 1 and grade 2 based on the location of exudates. Region of macula is marked by Early Treatment Diabetic Retinopathy Studies (ETDRS) grading scale. The proposed method is evaluated using 200 images of publically available MESSIDOR database and performance figures of 91% for sensitivity, 98% for specificity and 94.5% for accuracy are obtained.

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Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

Keerthiveena

Esakkirajan

Selvakumar

et al. 2019

Int J Imaging Syst Tech

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In this article, an attempt is made to combine both transform and spatial domain features to increase the accuracy, sensitivity, and specificity for the classification of retinal diseases. Papilloedema, macular edema, glaucoma, diabetic retinopathy (DR), and central retinal vein occlusion (CRVO) are the leading cause of blindness in humans. To identify these diseases in a mass screening process, it consumes intense labor and time. In this proposed work, an automated computer aided algorithm is developed to classify the normal from the abnormal (Papilledema, macular edema, glaucoma, DR, and CRVO) images. For any classification task, an increase in data size, number of classes, and dimension of the feature space affect the performance of any classifier.A single classifier is generally unable to handle the wide variability and scalability of the data in any problem. In the proposed algorithm, the feature fusion technique is employed to reduce the input data to the classifier. The features are extracted from wavelet packet transform (WPT) and intensity hue saturation (IHS). A support vector machine (SVM)-based learning algorithm is used to train the classifier using the extracted features. The proposed method yields better results than the existing classifier fusion method with an overall accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and area under ROC curve (AUC) of 96.3%, 95.8%, 97.8%, 96.4%,98.1%, and 0.971 respectively. K E Y W O R D Sfeature fusion, intensity hue saturation, support vector machine, wavelet packet transform, weighted principal component analysis

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Automatic classification of diabetic macular edema in digital fundus images

Cited by 56 publications

References 7 publications

Diabetic Macular Edema Grading Based on Deep Neural Networks

Diabetic Macular Edema Grading Based on Deep Neural Networks

Severity Grading of DME from Retina Images: A Combination of PSO and FCM with Bayes Classifier

Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

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