Author:
Krishnamoorthy Sujatha,Weifeng Yu,Luo Jingling,Kardy Seifedine
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics
Reference34 articles.
1. Chaturvedi SS, Gupta K, Ninawe V, Prasad PS (2020) Automated diabetic retinopathy grading using deep convolutional neural network. arXiv preprint arXiv:2004.06334.
2. Das S, Kharbanda K, Suchetha M, Raman R, Dhas E (2021) Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control 68:102600
3. Desika Vinayaki V, Kalaiselvi R (2023) ESLO: Enhanced sea lion optimization based bi‐directional CNN‐RNN for accurate detection of diabetic retinopathy. Concurr Comput: Pract Exp p e7391.
4. Dhakal A, Bastola LP, Shakya S (2019) Detection and classification of diabetic retinopathy using adaptive boosting and artificial neural network. Int J Adv Res Publ (IJARP) 3(8):191–196. http://www.ijarp.org/online-papers-publishing/aug2019.html
5. Eren L, Ince T, Kiranyaz S (2019) A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J Signal Process Syst 91(2):179–189