Study on Fine-Grained Visual Classification of Low-Resolution Urinary Erythrocyte.
The morphological analysis test item of urine red blood cells is referred to as "extracorporeal renal biopsy," which holds significant importance for medical department testing. However, the accuracy of existing urine red blood cell morphology analyzers is suboptimal, and they are not widely utilize...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2513 - 2524 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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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=181515394&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515394 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515394 181515394 181515394 10.1007/s10278-024-01082-1 181515394 ppf: 2513 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Study on Fine-Grained Visual Classification of Low-Resolution Urinary Erythrocyte. aug: au: Ji, Qingbo Yin, Tingshuo Zhang, Pengfei Liu, Qingquan Hou, Changbo affil: https://ror.org/03x80pn82 College of information and Communication Engineering, Harbin Engineering University, Harbin, China sug: subj: Erythrocytes Analysis Urinalysis Erythrocytes Classification Sensitivity and Specificity Evaluation Image Processing, Computer Assisted Methods Digital Imaging Methods Detection Algorithms Experimental Studies Descriptive Statistics Funding Source Automation Neural Networks (Computer) Deep Learning ab: The morphological analysis test item of urine red blood cells is referred to as "extracorporeal renal biopsy," which holds significant importance for medical department testing. However, the accuracy of existing urine red blood cell morphology analyzers is suboptimal, and they are not widely utilized in medical examinations. Challenges include low image spatial resolution, blurred distinguishing features between cells, difficulty in fine-grained feature extraction, and insufficient data volume. This article aims to improve the classification accuracy of low-resolution urine red blood cells. This paper proposes a super-resolution method based on category-aware loss and an RBC-MIX data enhancement approach. It optimizes the cross-entropy loss to maximize the classification boundary and improve intra-class tightness and inter-class difference, achieving fine-grained classification of low-resolution urine red blood cells. Experimental outcomes demonstrate that with this method, an accuracy rate of 97.8% can be achieved for low-resolution urine red blood cell images. This algorithm attains outstanding classification performance for low-resolution urine red blood cells with only category labels required. This method can serve as a practical reference for urine red blood cell morphology examination items. 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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