Artificial Humming Bird Optimization–Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images-Reference-Cited by-同舟云学术

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Artificial Humming Bird Optimization–Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images

Published:2022-10-14 Issue:1 Volume:36 Page:59-72
ISSN:1618-727X
Container-title:Journal of Digital Imaging
language:en
Short-container-title:J Digit Imaging

Author:

E. Dhiravidachelvi,S. Senthil Pandi,R. Prabavathi,C. Bala Subramanian

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

https://link.springer.com/content/pdf/10.1007/s10278-022-00707-7.pdf

Reference33 articles.

1. Auccahuasi W, Flores E, Sernaque F, Cueva J, Diaz M, Oré E: Recognition of hard exudates using Deep Learning. Procedia Computer Science. 167: 2343-2353, 2020

2. Bilal A, Sun G, Mazhar S, Imran A, Latif J: A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. pp.1–12, 2022

3. Kadan AB, Subbian PS, Detection of hard exudates using evolutionary feature selection in retinal fundus images. Journal of Medical Systems, 43(7):1-12, 2019

4. Anitha GJ, Maria KG: Detecting hard exudates in retinal fundus images using convolutional neural networks. In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) (pp. 1–5). IEEE, 2018

5. Benzamin A, Chakraborty C: Detection of hard exudates in retinal fundus images using deep learning. In 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) (pp. 465–469). IEEE.2018

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