Proposed retinal abnormality detection and classification approach: Computer aided detection for diabetic retinopathy by machine learning approaches

Raman, Valliappan; Then, Patrick; Sumari, Putra

doi:10.1109/iccsn.2016.7586601

Cited by 36 publications

(11 citation statements)

References 8 publications

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“…Majority of these artificial neural networks (ANN)-based approaches [11][12][13] do not address the overfitting issues, particularly for large-scale fundus images. In [14], the authors applied optic disc identification for exudates and micro-aneurysm extraction-based DR, where they performed a five-class classification: mild, moderate, severe, NPDR, and PDR. To localize exudates, authors used a genetic algorithm [15].…”

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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“…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. To identify the exudates in the DR images, the genetic algorithm was used by [26].…”

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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“…Below few of the related works has been discussed. In [3], used the computed based method for identification of abnormality in the retinal fundus images. Author has introduced methodology that initially applies the process of noise removal so the quality of image is enhanced.…”

Section: Introductionmentioning

confidence: 99%

Identification of Diabetic Retinopathy from fundus images using CNNs

Math*¹,

Fatima²

2019

IJITEE

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the Diabetic Retinopathy is the diabetes-mellitus to human vision that is the main cause of vision loss. The early stage detection of diabetic retinopathy is can play eminent role in the diabetes treatment. The fundus of retinal image is utilized to recognize the symptoms of diabetic retinopathy. Moreover, the above phenomena led us to propose this paper; here we propose segment based learning approach for identification of diabetic retinopathy. The segment based image level is required to obtain the identification of diabetic retinopathy images, the classifiers and features are equally learned from the data. Then, we adapt pre-trained CNN as the fine tune to achieve the segment level estimation of diabetic retinopathy. For identification of diabetic retinopathy, we achieve accuracy 96.97 and 98.46% at 20 and 30% and also achieve AUC (Area under Curve) 97.51 and 98.50 at 20 and 30% on the Kaggle dataset. Our proposed model outperforms much better than other models.

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Proposed retinal abnormality detection and classification approach: Computer aided detection for diabetic retinopathy by machine learning approaches

Cited by 36 publications

References 8 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

Identification of Diabetic Retinopathy from fundus images using CNNs

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