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

By Muhammad Mateen, Junhao Wen, Nasrullah, Sun Song, Zhouping Huang. Symmetry, 2018

507 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Health Information Management, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2b1991da2a10b30518c36a0020d18019a055ec42

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In Computer Vision and Pattern Recognition

1. Po-Yao (Bernie) Huang
2. Zhihui Li
3. Xiaojun Chang
4. Xin Wang
5. Xiaojiang Chen
6. Yun Xiao
7. Pengzhen Ren

Citations in the corpus

• A Survey of Deep Active Learning (2020, background): “Thanks to the publication of a large number of existing annotation datasets [16, 87], in recent years, DL has made breakthroughs in various fields including machine translation [4, 13, 159, 168], speech recognition [110, 116, 120, 136], and image classification [60, 106, 115, 174].”