{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T12:10:15Z","timestamp":1775218215413,"version":"3.50.1"},"reference-count":33,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":272,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats: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\u2010range 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\u2010range dependencies. Experimental results show that our model achieves test accuracies of 88.49% and 77.33% on the augmented APTOS 2019 and Messidor\u20102 datasets, respectively, validating the efficacy of the proposed mechanism.<\/jats:p>","DOI":"10.1049\/ipr2.70220","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T06:53:52Z","timestamp":1759301632000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Detection of Diabetic Retinopathy by Using Global Channel Attention Mechanism"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5259-0356","authenticated-orcid":false,"given":"Jing","family":"Qin","sequence":"first","affiliation":[{"name":"School of Economics and Management Huanghuai University  Zhumadian Henan Province China"}]},{"given":"Xiaolong","family":"Bu","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence Huanghuai University  Zhumadian Henan Province China"}]}],"member":"265","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.1077669"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc11\u20101909"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40662\u2010015\u20100026\u20102"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41433\u2010024\u201003101\u20105"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/ceo.12696"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.1049\/cvi2.12116"},{"key":"e_1_2_15_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3415617"},{"key":"e_1_2_15_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.1079217"},{"key":"e_1_2_15_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332"},{"key":"e_1_2_15_11_1","doi-asserted-by":"crossref","unstructured":"K.He X.Zhang S.Ren andJ.Sun \u201cDeep Residual Learning for Image Recognition \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (arXiv 2016) 770\u2013778 https:\/\/doi.org\/10.48550\/arXiv.1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_15_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23125726"},{"key":"e_1_2_15_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106168"},{"key":"e_1_2_15_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12046\u2010023\u201002175\u20103"},{"key":"e_1_2_15_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3070685"},{"key":"e_1_2_15_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123523"},{"key":"e_1_2_15_17_1","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12155"},{"key":"e_1_2_15_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2024.3404360"},{"key":"e_1_2_15_19_1","doi-asserted-by":"crossref","unstructured":"J.Hu L.Shen andG.Sun \u201cSqueeze\u2010and\u2010Excitation Networks \u201d inProceedings of the2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (IEEE 2018) 7132\u20137141 https:\/\/doi.org\/10.1109\/CVPR.2018.00745.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_15_20_1","doi-asserted-by":"crossref","unstructured":"S.Woo J.Park J.\u2010Y.Lee andI. S.Kweon CBAM: Convolutional Block Attention Module inComputer Vision \u2013 ECCV 2018 eds. V. Ferrari M. Hebert C. Simenchisescu and Y. Weiss Lecture Notes in Computer Science 11211 (Springer 2018) 3\u201319 https:\/\/doi.org\/10.1007\/978\u20103\u2010030\u201001234\u20102_1.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_2_15_21_1","unstructured":"A.Dosovitskiy L.Beyer A.Kolesnikov et\u00a0al. An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv Preprint arXiv:2010.11929 October 22 2020 https:\/\/doi.org\/10.48550\/arXiv.2010.11929."},{"key":"e_1_2_15_22_1","unstructured":"Karthik Maggie andS.Dane APTOS 2019 Blindness Detection. Kaggle (2019) https:\/\/kaggle.com\/competitions\/aptos2019\u2010blindness\u2010detection."},{"key":"e_1_2_15_23_1","doi-asserted-by":"publisher","DOI":"10.5566\/ias.1155"},{"key":"e_1_2_15_24_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"e_1_2_15_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2018.01.034"},{"key":"e_1_2_15_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15312"},{"key":"e_1_2_15_27_1","doi-asserted-by":"crossref","unstructured":"S. H.Kassani P. H.Kassani R.Khazaeinezhad M. J.Wesolowski K. A.Schneider andR.Deters \u201cDiabetic Retinopathy Classification Using a Modified Xception Architecture \u201d inProceedings of the2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (IEEE 2019) 1\u20136 https:\/\/doi.org\/10.1109\/ISSPIT47144.2019.9001846.","DOI":"10.1109\/ISSPIT47144.2019.9001846"},{"key":"e_1_2_15_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-5788-0_64"},{"key":"e_1_2_15_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105602"},{"key":"e_1_2_15_30_1","doi-asserted-by":"crossref","unstructured":"N.Sarnaik A.Gautam S.Kushwaha andR.Shanker \u201cConvolutional Vision Transformer Based Automatic Grading of Diabetic Retinopathy Images \u201d inProceedings of the2024 IEEE 8th International Conference on Information and Communication Technology (CICT) (IEEE 2024) 1\u20135 https:\/\/doi.org\/10.1109\/CICT64037.2024.10899535.","DOI":"10.1109\/CICT64037.2024.10899535"},{"key":"e_1_2_15_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3312709"},{"key":"e_1_2_15_32_1","unstructured":"Z.Dai H.Liu Q. V.Le andM.Tan \"CoAtNet: Marrying Convolution and Attention for All Data Sizes \" arXiv preprint arXiv:2106.04803 September 15 2021 https:\/\/doi.org\/10.48550\/arXiv.2106.04803."},{"key":"e_1_2_15_33_1","unstructured":"Z.Liu Y.Lin Y.Cao et\u00a0al. \"Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows \" arXiv preprint arXiv: 2103.14030 August 17 2021 https:\/\/doi.org\/10.48550\/arXiv.2103.14030."},{"key":"e_1_2_15_34_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14198823"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70220","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ipr2.70220","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70220","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T11:36:34Z","timestamp":1775216194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.70220"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/ipr2.70220"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.70220","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"value":"1751-9659","type":"print"},{"value":"1751-9667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-06-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70220"}}