Deep-Optimal Leucorrhea Detection Through Fluorescent Benchmark Data Analysis.
Vaginitis is a common condition in women that is described medically as irritation and/or inflammation of the vagina; it poses a significant health risk for women, necessitating precise diagnostic methods. Presently, conventional techniques for examining vaginal discharge involve the use of wet moun...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 6; pp. 4059 - 4075 |
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| Main Authors: | , , , , , , , , , , , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190236355&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190236355 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Dec2025 vid: 38 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 190236355 189894317 190236355 190236355 10.1007/s10278-025-01428-3 190236355 ppf: 4059 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep-Optimal Leucorrhea Detection Through Fluorescent Benchmark Data Analysis. aug: au: Li, Shuang Omer, Akam M. Duan, Yuping Fang, Qiang Hamad, Kamyar Othman Fernandez, Mauricio Lin, Ruiqing Wen, Jianghua Wang, Yanping Cai, Jingang Guo, Guangchao Wu, Yingying Yi, Fang Meng, Jianqiao Mao, Zhiqun Duan, Yuxia affil: https://ror.org/00f1zfq44 School of Physics, Central South University, 932 Lushan South Road, 410083, Changsha, Hunan, China sug: subj: Leukorrhea Diagnosis Fluorescent Dyes Staining and Labeling Benchmarking Human Deep Learning Convolutional Neural Networks Descriptive Statistics Automation Microscopy Funding Source ab: Vaginitis is a common condition in women that is described medically as irritation and/or inflammation of the vagina; it poses a significant health risk for women, necessitating precise diagnostic methods. Presently, conventional techniques for examining vaginal discharge involve the use of wet mounts and gram staining to identify vaginal diseases. In this research, we utilized fluorescent staining, which enables distinct visualization of cellular and pathogenic components, each exhibiting unique color characteristics when exposed to the same light source. We established a large, challenging multiple fluorescence leucorrhea dataset benchmark comprising 8 categories with a total of 343 K high-quality labels. We also presented a robust lightweight deep-learning network, LRNet. It includes a lightweight feature extraction network that employs Ghost modules, a feature pyramid network that incorporates deformable convolution in the neck, and a single detection head. The evaluation results indicate that this detection network surpasses conventional networks and can cut down the model parameters by up to 91.4% and floating-point operations (FLOPs) by 74%. The deep-optimal leucorrhea detection capability of LRNet significantly enhances its ability to detect various crucial indicators related to vaginal health. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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