A deep integrative approach for diabetic retinopathy classification with synergistic channel-spatial and self-attention mechanism-Reference-Cited by-同舟云学术

A deep integrative approach for diabetic retinopathy classification with synergistic channel-spatial and self-attention mechanism

Published:2024-09 Issue: Volume:249 Page:123523
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Madarapu Sandeep^ORCID,Ari Samit^ORCID,Mahapatra K.K.^ORCID

Publisher

Elsevier BV

Reference45 articles.

1. Performance evaluation of binary classification of diabetic retinopathy through deep learning techniques using texture feature;Adriman;Procedia Computer Science,2021

2. EDR-Net: Lightweight deep neural network architecture for detecting referable diabetic retinopathy;Aujih;IEEE Transactions on Biomedical Circuits and Systems,2022

3. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: A clinical validation study;Bellemo;The Lancet Digital Health,2019

4. Deep learning–based diabetic retinopathy severity grading system employing quadrant ensemble model;Bhardwaj;Journal of Digital Imaging,2021

5. Self-adaptive stacking ensemble approach with attention based deep neural network models for diabetic retinopathy severity prediction;Bodapati;Multimedia Tools and Applications,2023

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DFCAFNet: Dual-feature co-attentive fusion network for diabetic retinopathy grading;Biomedical Signal Processing and Control;2024-10

2. Evaluating the performance of a non-uniform squash function in Capsule networks for early diabetic retinopathy detection using fundus image analysis;Results in Engineering;2024-09

3. Two-stage deep learning classification for diabetic retinopathy using gradient weighted class activation mapping;Automatika;2024-06-27