{"title":"Automated Detection of Diabetic Retinopathy by Using Global Channel Attention Mechanism","authors":"Jing Qin, Xiaolong Bu","doi":"10.1049/ipr2.70220","DOIUrl":null,"url":null,"abstract":"<p>Diabetic retinopathy (DR), a major ocular complication of diabetes, poses a significant global health challenge. Although convolutional neural networks (CNNs) have demonstrated effectiveness in DR grading tasks, their ability to capture long-range dependencies scattered across fundus images remains limited. To address this limitation, we propose a global channel attention mechanism that incorporates the global feature extraction capability of Vision Transformer (ViT) while maintaining compatibility with CNN architectures, thereby enhancing their ability to model long-range dependencies. Experimental results show that our model achieves test accuracies of 88.49% and 77.33% on the augmented APTOS 2019 and Messidor-2 datasets, respectively, validating the efficacy of the proposed mechanism.</p>","PeriodicalId":56303,"journal":{"name":"IET Image Processing","volume":"19 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ipr2.70220","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Image Processing","FirstCategoryId":"94","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ipr2.70220","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Diabetic retinopathy (DR), a major ocular complication of diabetes, poses a significant global health challenge. Although convolutional neural networks (CNNs) have demonstrated effectiveness in DR grading tasks, their ability to capture long-range dependencies scattered across fundus images remains limited. To address this limitation, we propose a global channel attention mechanism that incorporates the global feature extraction capability of Vision Transformer (ViT) while maintaining compatibility with CNN architectures, thereby enhancing their ability to model long-range dependencies. Experimental results show that our model achieves test accuracies of 88.49% and 77.33% on the augmented APTOS 2019 and Messidor-2 datasets, respectively, validating the efficacy of the proposed mechanism.
期刊介绍：
The IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and display, enhancement and restoration, segmentation, colour and texture analysis, coding and communication, implementations and architectures as well as innovative applications.
Principal topics include:
Generation and Display - Imaging sensors and acquisition systems, illumination, sampling and scanning, quantization, colour reproduction, image rendering, display and printing systems, evaluation of image quality.
Processing and Analysis - Image enhancement, restoration, segmentation, registration, multispectral, colour and texture processing, multiresolution processing and wavelets, morphological operations, stereoscopic and 3-D processing, motion detection and estimation, video and image sequence processing.
Implementations and Architectures - Image and video processing hardware and software, design and construction, architectures and software, neural, adaptive, and fuzzy processing.
Coding and Transmission - Image and video compression and coding, compression standards, noise modelling, visual information networks, streamed video.
Retrieval and Multimedia - Storage of images and video, database design, image retrieval, video annotation and editing, mixed media incorporating visual information, multimedia systems and applications, image and video watermarking, steganography.
Applications - Innovative application of image and video processing technologies to any field, including life sciences, earth sciences, astronomy, document processing and security.
Current Special Issue Call for Papers:
Evolutionary Computation for Image Processing - https://digital-library.theiet.org/files/IET_IPR_CFP_EC.pdf
AI-Powered 3D Vision - https://digital-library.theiet.org/files/IET_IPR_CFP_AIPV.pdf
Multidisciplinary advancement of Imaging Technologies: From Medical Diagnostics and Genomics to Cognitive Machine Vision, and Artificial Intelligence - https://digital-library.theiet.org/files/IET_IPR_CFP_IST.pdf
Deep Learning for 3D Reconstruction - https://digital-library.theiet.org/files/IET_IPR_CFP_DLR.pdf
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