Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning

被引：22

作者：

Fayyaz, Abdul Muiz ^{[1
]}

Sharif, Muhammad Imran ^{[2
]}

Azam, Sami ^{[3
]}

Karim, Asif ^{[3
]}

El-Den, Jamal ^{[3
]}

机构：

[1] Univ Wah, Dept Comp Sci, Wah 47040, Pakistan

[2] COMSATS Univ Islamabad, Dept Comp Sci, Wah 47040, Pakistan

[3] Charles Darwin Univ, Fac Sci & Technol, Darwin 0909, Australia

来源：

INFORMATION | 2023年 / 14卷 / 01期

关键词：

deep learning; classification; diabetic retinopathy; AlexNet; Resnet101; FUNDUS IMAGE;

D O I：

10.3390/info14010030

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%.

引用

页数：14

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