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Symmetry 2019, 11(1), 1; https://doi.org/10.3390/sym11010001

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

School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China
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Received: 22 November 2018 / Revised: 11 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
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Abstract

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. View Full-Text
Keywords: deep convolutional neural network; diabetic retinopathy; fundus images; VGGNet DNN; PCA; SVD deep convolutional neural network; diabetic retinopathy; fundus images; VGGNet DNN; PCA; SVD
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Mateen, M.; Wen, J.; Nasrullah; Song, S.; Huang, Z. Fundus Image Classification Using VGG-19 Architecture with PCA and SVD. Symmetry 2019, 11, 1.

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