Segmentation and Detection of Diabetic Retinopathy Exudates

Elbalaoui, Abderrahmane; Fakir, Mohamed; Merbouha, Abdelkrim

doi:10.5120/15963-5155

Cited by 5 publications

(8 citation statements)

References 9 publications

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“…The authors suggested a back-propagation-based neural network for twoclass classifications. Similarly, in [12], multilayer perception neural network (MLPNN) was used to detect DR. An automatic exudate detection approach was developed in [13], where optic disk (OD) segmentation was performed using a graph cuts algorithm. The authors in [13] used the invariant moments Hu to extract the feature vector and NN-based two classes; exudate and non-exudate classification was performed.…”

Section: Literature Surveymentioning

confidence: 99%

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

2017

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The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)-and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy-94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR. Keywords Computer-aided diagnosis Á Diabetic retinopathy Á Deep neural network Á AlexNet DNN Á Convolutional neural network Á Gaussian mixture model Á Linear discriminant analysis Á SVM

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Section: Literature Surveymentioning

confidence: 99%

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

2017

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“…Bhatkar and Kharat [23] applied a deep neural network based on multilayer perception. On the basis of optic disk segmentation, an automatic computer aided detection approach was developed [24] with the graph cuts technique. Raman et al [25], used optic disk identification for microaneurysm and exudate features extraction to classify the DR images.…”

Section: Introductionmentioning

confidence: 99%

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

et al. 2018

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Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.

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“…The image containing exudates are shown in Figure 1. Elbalaoui, M. Fakir, and A. Merbouha [1] proposed an automated method for the detection of exudates in retinal color fundus images. First, the image is converted to HSI model, after pre-processing possible regions containing exudate, the optic disk is detected using Hough transform.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Classification of Retinal Image Features

Prasad¹,

Virani²

2017

IJERT

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Retinal image analysis is becoming an important screening tool for the early revealing of certain risks and diseases such as Diabetic Retinopathy (DR). The main aim of this paper is to detect Diabetic Retinopathy. DR, also known as diabetic eye disease, is when damage occurs to the retina due to diabetes. It can eventually lead to blindness so it becomes necessary to develop a novel solution for easy detection of DR. It occurs when small blood vessels get damaged in the retina, due to the high glucose level. Exudates are the major sign of DR and the detection of exudates is the fundamental requirement to diagnose the progress of DR. Direct inspection of retinal image is not sufficient for the detection of DR and also requires periodic screening which is a time-consuming so this paper presents an automated process to detect DR through detecting exudate in fundus images. First, the image is converted into a grey scale which is followed by Adaptive histogram technique after preprocessing edge detection is done to detect exudates and then relevant features are extracted and finally, classification of DR is done using SVM classifier.

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Segmentation and Detection of Diabetic Retinopathy Exudates

Cited by 5 publications

References 9 publications

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

Segmentation and Classification of Retinal Image Features

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