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ECA-CBAM: Classification of Diabetic Retinopathy: Classification of diabetic retinopathy by cross-combined attention mechanism

Published: 04 June 2022 Publication History

Abstract

Although there is no distinctive header, this is the abstract. Diabetic retinopathy is an ophthalmological disease that causes bleeding in the fundus and loss of vision due to damage to blood vessels in the retina. It is one of the main causes of vision loss in the world. To slow down the development of the disease, early screening of the eyeball is needed. This paper proposes a new method of classification, automatic screening and accurate diagnosis of diabetic retinopathy based on convolutional neural network. Specifically, five attention mechanisms such as BAM, CBAM, ECA, CA and SeNet are used to classify diabetic retinopathy. Through comparative experiments, it is found that ECA-CBAM cross-combination model has the best classification performance.

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  • (2023)Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy ImagesElectronics10.3390/electronics1204086212:4(862)Online publication date: 8-Feb-2023
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cover image ACM Other conferences
ICIAI '22: Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence
March 2022
240 pages
ISBN:9781450395502
DOI:10.1145/3529466
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2022

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Author Tags

  1. Attention Mechanism
  2. Convolutional Neural Network
  3. Cross Combination
  4. Fundus Image Classification

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • National Natural Science Foundation of China

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ICIAI 2022

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Cited By

View all
  • (2024)The Internet of Things Drives Smart City ManagementJournal of Organizational and End User Computing10.4018/JOEUC.33821436:1(1-17)Online publication date: 7-Feb-2024
  • (2024)PCA: Progressive class-wise attention for skin lesions diagnosisEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107417127(107417)Online publication date: Jan-2024
  • (2023)Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy ImagesElectronics10.3390/electronics1204086212:4(862)Online publication date: 8-Feb-2023
  • (2023)Performance Analysis of Deep Learning based Segmentation of Retinal Lesions in Fundus Images2023 Second International Conference on Electronics and Renewable Systems (ICEARS)10.1109/ICEARS56392.2023.10085616(1306-1313)Online publication date: 2-Mar-2023
  • (2023)Dynamic Convolutional Attention for Classification of Diabetic Retinopathy2023 IEEE 7th Conference on Information and Communication Technology (CICT)10.1109/CICT59886.2023.10455722(1-6)Online publication date: 15-Dec-2023
  • (2022)Deep Learning for Diabetic Retinopathy in Fundus Images2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo)10.1109/CINTI-MACRo57952.2022.10029554(000351-000358)Online publication date: 21-Nov-2022
  • (2022)An Improved YOLOv3 Algorithm and Intruder Detection on Transmission Line2022 China Automation Congress (CAC)10.1109/CAC57257.2022.10055158(5736-5741)Online publication date: 25-Nov-2022

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