{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T17:41:00Z","timestamp":1776879660566,"version":"3.51.2"},"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["2020D01C034"],"award-info":[{"award-number":["2020D01C034"]}]},{"name":"Tianshan Innovation Team of Xinjiang Uygur Autonomous Region","award":["2020D14044"],"award-info":[{"award-number":["2020D14044"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["U1903213, 61771416 and 62041110"],"award-info":[{"award-number":["U1903213, 61771416 and 62041110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Creative Research Groups of Higher Education of Xinjiang Uygur Autonomous Region","award":["XJEDU2017T002"],"award-info":[{"award-number":["XJEDU2017T002"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.<\/jats:p>","DOI":"10.1371\/journal.pone.0261285","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T18:28:57Z","timestamp":1639679337000},"page":"e0261285","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":23,"title":["MHANet: A hybrid attention mechanism for retinal diseases classification"],"prefix":"10.1371","volume":"16","author":[{"given":"Lianghui","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0210-2273","authenticated-orcid":true,"given":"Liejun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuli","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongming","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"issue":"5","key":"pone.0261285.ref001","doi-asserted-by":"crossref","first-page":"165","DOI":"10.4239\/wjd.v4.i5.165","article-title":"Current status in diabetic macular edema treatments","volume":"4","author":"Pedro Romero-Aroca","year":"2013","journal-title":"World journal of diabetes"},{"issue":"10","key":"pone.0261285.ref002","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1016\/j.ophtha.2018.04.007","article-title":"Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings","volume":"125","author":"Tien Y Wong","year":"2018","journal-title":"Ophthalmology"},{"issue":"3","key":"pone.0261285.ref003","doi-asserted-by":"crossref","first-page":"354","DOI":"10.20471\/acc.2016.55.03.02","article-title":"Changes of drusen number and central retinal thickness in age-related macular degeneration patients over two years","volume":"55","author":"Kre\u0161imir Mandi\u0107","year":"2016","journal-title":"Acta Clin Croat"},{"issue":"4","key":"pone.0261285.ref004","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1001\/archopht.122.4.564","article-title":"Prevalence of age-related macular degeneration in the United States","volume":"122","author":"David S Friedman","year":"2004","journal-title":"Arch ophthalmol"},{"issue":"2","key":"pone.0261285.ref005","first-page":"142","article-title":"Early detection and treatment of neovascular age-related macular degeneration","volume":"15","author":"Neil M Bressler","year":"2002","journal-title":"The Journal of the American Board of Family Practice"},{"issue":"8","key":"pone.0261285.ref006","doi-asserted-by":"crossref","first-page":"909","DOI":"10.2337\/diacare.17.8.909","article-title":"Preventive eye care in people with diabetes is cost-saving to the federal government: implications for health-care reform","volume":"17","author":"Jonathan C Javitt","year":"1994","journal-title":"Diabetes care"},{"key":"pone.0261285.ref007","article-title":"Diabetes care and research in Europe: the St","author":"Hendrik Michiel Jan Krans","year":"1992","journal-title":"Vincent Declaration action programme: implementation document"},{"issue":"1","key":"pone.0261285.ref008","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/ageing\/26.1.3","article-title":"Improving the care of elderly diabetic patients: the final report of the St Vincent Joint Task Force for Diabetes","volume":"26","author":"Timothy J Hendra","year":"1997","journal-title":"Age and ageing"},{"key":"pone.0261285.ref009","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Geert Litjens","year":"2017","journal-title":"Medical image analysis"},{"issue":"5035","key":"pone.0261285.ref010","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1126\/science.1957169","article-title":"Optical coherence tomography","volume":"254","author":"David Huang","year":"1991","journal-title":"science"},{"key":"pone.0261285.ref011","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Alex Krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"pone.0261285.ref012","doi-asserted-by":"crossref","unstructured":"Oulhaj, Hind and Amine, Aouatif and Rziza, Mohammed and Aboutajdine, Driss. Noise Reduction in Medical Images-comparison of noise removal algorithms. 2012 International Conference on Multimedia Computing and Systems. 2012;344\u2013349.","DOI":"10.1109\/ICMCS.2012.6320218"},{"issue":"4","key":"pone.0261285.ref013","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/TMI.2017.2780115","article-title":"Macular OCT classification using a multi-scale convolutional neural network ensemble","volume":"37","author":"Reza Rasti","year":"2017","journal-title":"IEEE transactions on medical imaging"},{"key":"pone.0261285.ref014","unstructured":"Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. 2014."},{"issue":"10","key":"pone.0261285.ref015","doi-asserted-by":"crossref","first-page":"3568","DOI":"10.1364\/BOE.5.003568","article-title":"Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images","volume":"5","author":"Pratul P Srinivasan","year":"2014","journal-title":"Biomedical optics express"},{"issue":"12","key":"pone.0261285.ref016","doi-asserted-by":"crossref","first-page":"4928","DOI":"10.1364\/BOE.7.004928","article-title":"Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images","volume":"7","author":"Yu Wang","year":"2016","journal-title":"Biomedical optics express"},{"issue":"2167","key":"pone.0261285.ref017","doi-asserted-by":"crossref","first-page":"20140081","DOI":"10.1098\/rspa.2014.0081","article-title":"Machine learning methods in the computational biology of cancer","volume":"470","author":"Mathukumalli Vidyasagar","year":"2014","journal-title":"Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"key":"pone.0261285.ref018","article-title":"Correlation-based feature subset selection for machine learning","author":"Mark A Hall","year":"1998","journal-title":"Thesis submitted in partial fulfillment of the requirements of the degree of Doctor of Philosophy at the University of Waikato"},{"issue":"2","key":"pone.0261285.ref019","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1364\/BOE.8.000579","article-title":"Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration","volume":"8","author":"Sri Phani Krishna Karri","year":"2017","journal-title":"Biomedical optics express"},{"issue":"3","key":"pone.0261285.ref020","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00417-018-04224-8","article-title":"Fully automated detection of retinal disorders by image-based deep learning","volume":"257","author":"Feng Li","year":"2019","journal-title":"Graefe\u2019s Archive for Clinical and Experimental Ophthalmology"},{"issue":"5","key":"pone.0261285.ref021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JPHOT.2019.2934484","article-title":"On OCT image classification via deep learning","volume":"11","author":"Depeng Wang","year":"2019","journal-title":"IEEE Photonics Journal"},{"key":"pone.0261285.ref022","unstructured":"Simonyan, Karen and Zisserman, Andrew. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014."},{"key":"pone.0261285.ref023","doi-asserted-by":"crossref","unstructured":"Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jon and Wojna, Zbigniew. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"pone.0261285.ref024","doi-asserted-by":"crossref","unstructured":"Yang, Yibo and Zhong, Zhisheng and Shen, Tiancheng and Lin, Zhouchen. Convolutional neural networks with alternately updated clique. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018;2413\u20132422.","DOI":"10.1109\/CVPR.2018.00256"},{"key":"pone.0261285.ref025","unstructured":"Chen, Yunpeng and Li, Jianan and Xiao, Huaxin and Jin, Xiaojie and Yan, Shuicheng and Feng, Jiashi. Dual path networks. arXiv preprint arXiv:1707.01629. 2017."},{"key":"pone.0261285.ref026","doi-asserted-by":"crossref","unstructured":"Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017;4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"pone.0261285.ref027","doi-asserted-by":"crossref","unstructured":"He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"pone.0261285.ref028","doi-asserted-by":"crossref","unstructured":"Xie, Saining and Girshick, Ross and Doll\u00e1r, Piotr and Tu, Zhuowen and He, Kaiming. Aggregated residual transformations for deep neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017;1492\u20131500.","DOI":"10.1109\/CVPR.2017.634"},{"key":"pone.0261285.ref029","doi-asserted-by":"crossref","unstructured":"Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian. Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021;13733\u201313742.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"pone.0261285.ref030","doi-asserted-by":"crossref","unstructured":"Hu, Jie and Shen, Li and Sun, Gang. Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018;7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"pone.0261285.ref031","doi-asserted-by":"crossref","unstructured":"Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Yang, Jian. Selective kernel networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019;510\u2013519.","DOI":"10.1109\/CVPR.2019.00060"},{"issue":"5","key":"pone.0261285.ref032","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Daniel S Kermany","year":"2019","journal-title":"Cell"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0261285","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T18:29:38Z","timestamp":1639679378000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0261285"}},"subtitle":[],"editor":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0261285","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}