{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T17:23:51Z","timestamp":1761672231810,"version":"build-2065373602"},"reference-count":24,"publisher":"Institution of Engineering and Technology (IET)","issue":"11","license":[{"start":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:00:00Z","timestamp":1688515200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703268"],"award-info":[{"award-number":["61703268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Diabetic retinopathy (DR) is one of the leading causes of blindness for people suffering from diabetes. Microaneurysm (MA) is the initial symptom of DR. MA is a lesion based disease which starts as small red spots on the retina and increases in size as the DR progresses which finally leads to blindness. So eliminating the lesion can effectively prevent DR at an early stage. However, due to complex retinal structure, different brightness and contrast of fundus images with effects of factors such as different patients, environment changes, and difference in acquisition equipment, it is difficult for existing detection algorithms to achieve accurate results of MA detection and location. Therefore, the detection algorithm of improved YOLOv4 (YOLOv4\u2010Pro) was proposed. First, an improved Fuzzy C\u2010Means (IFCM) clustering algorithm was proposed to optimize anchor parameters of target samples to improve matching results between anchors and feature graphs. In order to control noise and improve efficiency, a median filtering method was employed to update the criterion function of the original FCM algorithm, and a K\u2010means algorithm was employed to initialize clustering. Second, a SENet attention module was added in the backbone of YOLOv4 to enhance key information and suppress background, improving the confidence of MA effectively. Finally, the spatial pyramid pooling (SPP) module was added to the neck to enhance the acceptance domain of the output characteristics of the backbone network, and profits separating of important context information. The improved YOLOv4 with IFCM was verified on the Kaggle DR dataset and compared with other methods. Experimental results show that optimizing the prior frame with the IFCM algorithm can make it suitable to frame the Kaggle DR dataset, which improves the detection accuracy of the network by nearly 5%, and provides a nice performance on detection and location of MA in fundus images. This would help ophthalmologists finding the exact location of MA on retina, thereby simplifying the process and eliminating any manual intervention.<\/jats:p>","DOI":"10.1049\/ipr2.12867","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:25:08Z","timestamp":1688603108000},"page":"3349-3357","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Detection and location of microaneurysms in fundus images based on improved YOLOv4 with IFCM"],"prefix":"10.1049","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9314-7383","authenticated-orcid":false,"given":"Weiwei","family":"Gao","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering Shanghai University of Engineering Science Shanghai China"}]},{"given":"Bo","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering Shanghai University of Engineering Science Shanghai China"}]},{"given":"Yu","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering Shanghai University of Engineering Science Shanghai China"}]},{"given":"Mingtao","family":"Shan","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering Shanghai University of Engineering Science Shanghai China"}]},{"given":"Nan","family":"Song","sequence":"additional","affiliation":[{"name":"Eye&amp;ENT Hospital of Fudan University Shanghai China"}]}],"member":"265","published-online":{"date-parts":[[2023,7,5]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0161-6420(03)00475-5"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1243\/09544119JEIM486"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2017.02.012"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4557-0737-9.00047-3"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0084-9"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-020-01119-9"},{"key":"e_1_2_12_8_1","first-page":"3","article-title":"Automatic microaneurysm detection from non\u2010dilated diabetic retinopathy retinal images using mathematical morphology methods","volume":"38","author":"Sopharak A.","year":"2011","journal-title":"IAENG Int. J. Comput. Sci."},{"issue":"2","key":"e_1_2_12_9_1","first-page":"378","article-title":"Retinal microaneurysms detection using local convergence index features","volume":"39","author":"Dashtbozorg B.","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109084"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.02.016"},{"key":"e_1_2_12_12_1","unstructured":"Haloi M.:Improved microaneurysm detection using deep neural networks. arXiv preprint arXiv:1505.04424 (2015)"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.17-22721"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2794988"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13534-019-00136-6"},{"issue":"3","key":"e_1_2_12_16_1","first-page":"1","article-title":"FFU\u2010net: feature fusion u\u2010net for lesion segmentation of diabetic retinopathy","volume":"2021","author":"Xu Y.","year":"2021","journal-title":"Biomed Res. Int."},{"key":"e_1_2_12_17_1","doi-asserted-by":"crossref","unstructured":"Ghosh R. Ghosh K. Maitra S.:Automatic detection and classification of diabetic retinopathy stages using CNN. In:2017 4th International Conference on Signal Processing and Integrated Networks (SPIN).IEEE Piscataway(2017)","DOI":"10.1109\/SPIN.2017.8050011"},{"key":"e_1_2_12_18_1","unstructured":"Bochkovskiy A. Wang C.Y. Liao H.\u2010Y.M.:YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"e_1_2_12_19_1","doi-asserted-by":"crossref","unstructured":"Wang C.Y. Liao H.\u2010Y.M. Wu Y.H. et\u00a0al.:CSPNet: a new backbone that can enhance learning capability of CNN. In:2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp.1571\u20131580.IEEE Press New York(2020)","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"e_1_2_12_20_1","doi-asserted-by":"crossref","unstructured":"Huang G. Liu Z. van derMaaten L.: Densely connected convolutional networks. arXiv preprint arXiv:1608.069935v5 (2018)","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_12_21_1","unstructured":"Sutskever I. Martens J. Dahl G. et\u00a0al.:On the importance of initialization and momentum in deep learning. In:Proceedings of the 30th International Conference on Machine Learning International Machine Learning Society Madison WI(2013)"},{"key":"e_1_2_12_22_1","doi-asserted-by":"crossref","unstructured":"Smith L.N.:Cyclical learning rates for training neural networks. In:2017 IEEE Winter Conference on Applications of Computer Vision (WACV) pp.464\u2013472.IEEE Piscataway(2017)","DOI":"10.1109\/WACV.2017.58"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"e_1_2_12_24_1","doi-asserted-by":"crossref","unstructured":"Ghosh R. Ghosh K. Maitra S.:Automatic detection and classification of diabetic retinopathy stages using CNN. In:2017 4th International Conference on Signal Processing and Integrated Networks (SPIN).IEEE Piscataway(2017)","DOI":"10.1109\/SPIN.2017.8050011"},{"key":"e_1_2_12_25_1","doi-asserted-by":"crossref","unstructured":"Xiang Y. Mottaghi R. Savarese S.:Beyond pascal: a benchmark for 3d object detection in the wild. In:IEEE Winter Conference on Applications of Computer Vision.IEEE Piscataway(2014)","DOI":"10.1109\/WACV.2014.6836101"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.12867","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T17:08:53Z","timestamp":1761671333000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.12867"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,5]]},"references-count":24,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["10.1049\/ipr2.12867"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.12867","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"type":"print","value":"1751-9659"},{"type":"electronic","value":"1751-9667"}],"subject":[],"published":{"date-parts":[[2023,7,5]]},"assertion":[{"value":"2022-09-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}