A novel multi-scale network based on class attention for diabetes retinopathy

Hong Yu Chen, Rong Hua Wu, Chen Tao, Wen Jing Xu, Hui Yu, Hong Zhe Liu, Cheng Xu, Mu Wei Jian*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Downloads (Pure)


Diabetes Retinopathy (DR) is a common eye disease, which brings irreversible blindness risk to patients in severe cases. Due to the scarcity of professional ophthalmologists, developing computer-aided diagnostic systems to participate in DR grading diagnosis has become increasingly important. However, the current mainstream deep learning methods still cannot accurately classify the severity of DR, and their unreliable results are difficult to serve as a reference for clinicians. To tackle this problem, we propose two novel modules to improve the accuracy of DR classification. Specifically, we designed a multi-scale feature extraction module (MFEM) to capture tiny lesions in fundus images and differentiate similar lesions simultaneously. In addition, we also created a class attention module (CAM) to alleviate the adverse impact of intra-class similarity on DR grading. Experiment on the APTOS2019 blind detection dataset show that our proposed two modules have made significant improvements to the designed model, achieving state-of-the-art performance with 95.98% for ACC and 97.12% for QWK, respectively.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9798350319804
ISBN (Print)9798350319811
Publication statusPublished - 1 Mar 2024
Event9th IEEE Smart World Congress, SWC 2023 - Portsmouth, United Kingdom
Duration: 28 Aug 202331 Aug 2023


Conference9th IEEE Smart World Congress, SWC 2023
Country/TerritoryUnited Kingdom


  • Attention mechanism
  • DR grading
  • Fundus images
  • Multi-scale

Cite this