Detection and classification of clusters of microcalcifications on mammographic images.
An algorithm for detecting and classifying clusters of microcalcifications on mammograms is proposed. A feature of the algorithm proposed here is its potential for application to different types of calcifications and clusters of calcifications (both benign and suspicious). At the same time, vascular...
| Publicado en: | Biomedical Engineering Vol. 58; no. 1; pp. 40 - 45 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2024
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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=178029067&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178029067 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00063398 N96 jtl: Biomedical Engineering issn: 00063398 maglogo: N pubinfo: dt: May2024 vid: 58 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178029067 177261829 178029067 178029067 10.1007/s10527-024-10362-7 178029067 ppf: 40 ppct: 5 formats: tig: atl: Detection and classification of clusters of microcalcifications on mammographic images. aug: au: Pasynkov, D. V. Egoshin, I. A. Kolchev, A. A. Romanycheva, E. A. Klyushkin, I. V. Pasynkova, O. O. affil: https://ror.org/01yjw8d43 Department of Radiological Diagnostics and Oncology, Mari State University, Yoshkar-Ola, Republic of Mari El, Russian Federation sug: subj: Calcinosis Radiography Calcinosis Classification Mammography Image Processing, Computer Assisted Decision Trees Human Funding Source Breast Neoplasms Validity Sensitivity and Specificity Female Descriptive Statistics False Positive Results Decision Making, Clinical Female ab: An algorithm for detecting and classifying clusters of microcalcifications on mammograms is proposed. A feature of the algorithm proposed here is its potential for application to different types of calcifications and clusters of calcifications (both benign and suspicious). At the same time, vascular calcifications, which often give false positive results in algorithms, are analyzed separately, and a solution to this problem is proposed. The effectiveness of the proposed methods was assessed using a database of mammograms from patients with verified diagnoses. The classification algorithm achieved 96.15% accuracy, 95.32% specificity, and 98.21% sensitivity. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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