Vol.28, No.3, 2021, pp.729-737, doi:10.32604/iasc.2021.016569
OPEN ACCESS
ARTICLE
Improved Model of Eye Disease Recognition Based on VGG Model
  • Ye Mu1,2,3,4, Yuheng Sun1, Tianli Hu1,2,3,4, He Gong1,2,3,4, Shijun Li1,2,3,4,*, Thobela Louis Tyasi5
1 College of Information Technology, Jilin Agricultural University, Changchun, 130118, China
2 Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center, Changchun, 130118, China
3 Jilin Province Intelligent Environmental Engineering Research Center, Changchun, 130118, China
4 Jilin Province colleges and universities The 13th Five-Year Engineering Research Center, Changchun, 130118, China
5 Department of Agricultural Economics and Animal Production, University of Limpopo, 0727, Polokwane, South Africa
* Corresponding Author: Shijun Li. Email:
Received 05 January 2021; Accepted 14 February 2021; Issue published 20 April 2021
Abstract
The rapid development of computer vision technology and digital images has increased the potential for using image recognition for eye disease diagnosis. Many early screening and diagnosis methods for ocular diseases based on retinal images of the fundus have been proposed recently, but their accuracy is low. Therefore, it is important to develop and evaluate an improved VGG model for the recognition and classification of retinal fundus images. In response to these challenges, to solve the problem of accuracy and reliability of clinical algorithms in medical imaging this paper proposes an improved model for early recognition of ophthalmopathy in retinal fundus images based on the VGG training network of densely connected layers. To determine whether the accuracy and reliability of the proposed model were greater than those of previous models, our model was compared to ResNet, AlexNet, and VGG by testing them on a retinal fundus image dataset of eye diseases. The proposed model can ultimately help accelerate the diagnosis and referral of these early eye diseases, thereby facilitating early treatment and improved clinical outcomes.
Keywords
Deep learning model; dense block; eye disease recognition; fundus retina image; VGG
Cite This Article
Y. Mu, Y. Sun, T. Hu, H. Gong, S. Li et al., "Improved model of eye disease recognition based on vgg model," Intelligent Automation & Soft Computing, vol. 28, no.3, pp. 729–737, 2021.
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