Performances of CNN Architectures on Diabetic Retinopathy Detection Using Transfer Learning-Reference-Cited by-同舟云学术

Performances of CNN Architectures on Diabetic Retinopathy Detection Using Transfer Learning

Published:2022-06-16 Issue: Volume: Page:
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Container-title:2022 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)
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Author:

Kaya Esra¹,Saritas Ismail¹

Affiliation:

1. Selcuk University,Faculty of Technology,Electrical and Electronics Engineering Department,Konya,Türkiye

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9828466/9828468/09828576.pdf?arnumber=9828576

Reference10 articles.

1. Classification of diabetic retinopathy images based on customised CNN architecture;khan;2019 Amity International Conference on Artificial Intelligence (AICAI),2019

2. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures

3. Retinal vessel segmentation in colour fundus images using Extreme Learning Machine

4. Classification of Diabetic Retinopathy and Normal Retinal Images using CNN and SVM

5. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030;wild;Diabetes Care,2004

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1. A Multimodal Adversarial Database: Towards A Comprehensive Assessment of Adversarial Attacks and Defenses on Medical Images;2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA);2023-10-09

2. Diabetic Retinopathy Detection and Grading: A Transfer Learning Approach Using Simultaneous Parameter Optimization and Feature-Weighted ECOC Ensemble;IEEE Access;2023