Multi-label classification of fundus images based on graph convolutional network.
Background: Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but the detection of multiple lesions is still a difficult point in cur...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
7/30/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151896850&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151896850 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 7/30/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 151896850 151896850 NLM34330270 151896850 10.1186/s12911-021-01424-x NLM34330270 151896850 ppf: 1 ppct: 9 formats: tig: atl: Multi-label classification of fundus images based on graph convolutional network. aug: au: Yinlin Cheng Mengnan Ma Xingyu Li Yi Zhou Cheng, Yinlin Ma, Mengnan Li, Xingyu Zhou, Yi affil: School of Biomedical Engineering, Sun Yat-sen University, No. 132 Waihuan East Road, Guangzhou 510006, China. sug: subj: Diabetic Retinopathy Female Human Male Retina Algorithms Exudates and Transudates Comparative Studies Multicenter Studies Evaluation Research Validation Studies Interview Guides Funding Source Female Male ab: Background: Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but the detection of multiple lesions is still a difficult point in current research.Methods: This study proposed a multi-label classification method based on the graph convolutional network (GCN), so as to detect 8 types of fundus lesions in color fundus images. We collected 7459 fundus images (1887 left eyes, 1966 right eyes) from 2282 patients (1283 women, 999 men), and labeled 8 types of lesions, laser scars, drusen, cup disc ratio ([Formula: see text]), hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates. We constructed a specialized corpus of the related fundus lesions. A multi-label classification algorithm for fundus images was proposed based on the corpus, and the collected data were trained.Results: The average overall F1 Score (OF1) and the average per-class F1 Score (CF1) of the model were 0.808 and 0.792 respectively. The area under the ROC curve (AUC) of our proposed model reached 0.986, 0.954, 0.946, 0.957, 0.952, 0.889, 0.937 and 0.926 for detecting laser scars, drusen, cup disc ratio, hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates, respectively.Conclusions: Our results demonstrated that our proposed model can detect a variety of lesions in the color images of the fundus, which lays a foundation for assisting doctors in diagnosis and makes it possible to carry out rapid and efficient large-scale screening of fundus lesions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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