Improved ResNet_101 assisted attentional global transformer network for automated detection and classification of diabetic retinopathy disease
Author:
Publisher
Elsevier BV
Subject
Health Informatics,Signal Processing,Biomedical Engineering
Reference37 articles.
1. Key milestones in the diabetes research: a comprehensive update;Gupta;Obes. Med.,2020
2. Multitasking deep learning model for detection of five stages of diabetic retinopathy;Majumder;IEEE Access,2021
3. Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy;Das;Biomed. Signal Process. Control,2021
4. An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation;Kumar;Opt. Laser Technol.,2020
5. Lesion-aware transformers for diabetic retinopathy grading;Sun,2021
Cited by 2 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. Classification of diabetic retinopathy based on Functional Linked Neural network utilizing segmented fundus image features;Biomedical Signal Processing and Control;2024-09
2. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification;Physics in Medicine & Biology;2024-02-12
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