Automated Grading in Diabetic Retinopathy Using Image Processing and Modified EfficientNet-Reference-Cited by-同舟云学术

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Automated Grading in Diabetic Retinopathy Using Image Processing and Modified EfficientNet

Published:2020 Issue: Volume: Page:505-515
ISSN:0302-9743
Container-title:Computational Collective Intelligence
language:
Short-container-title:

Author:

Pham Hung N.,Tan Ren Jie,Cai Yu Tian,Mustafa Shahril,Yeo Ngan Chong,Lim Hui Juin,Do Trang T. T.,Nguyen Binh P.,Chua Matthew Chin Heng

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-63007-2_39

Reference41 articles.

1. The International Diabetes Federation: IDF Diabetes Atlas 7th Edition (2015)

2. Nguyen, B.P., Pham, H.N., et al.: Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. Comput. Methods Programs Biomed. 182, 105055 (2019)

3. Wild, S., Roglic, G., et al.: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–1053 (2004)

4. Wilkinson, C., Ferris III, F.L., et al.: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9), 1677–1682 (2003)

5. Beagley, J., Guariguata, L., et al.: Global estimates of undiagnosed diabetes in adults. Diabetes Res. Clin. Pract. 103(2), 150–160 (2014)

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2. Classification and Segmentation of Diabetic Retinopathy: A Systemic Review;Applied Sciences;2023-02-28

3. Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy Images;Electronics;2023-02-08

4. Comparison of Image Pre-processing for Classifying Diabetic Retinopathy Using Convolutional Neural Networks;Hybrid Intelligent Systems;2022

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