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

Abdul Muiz Fayyaz, Muhammad Imran Sharif, Sami Azam, Asif Karim, Jamal El-Den

Research output: Contribution to journalArticlepeer-review

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Abstract

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%.

Original languageEnglish
Article number30
Pages (from-to)1-14
Number of pages14
JournalInformation (Switzerland)
Volume14
Issue number1
DOIs
Publication statusPublished - Jan 2023

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