Discrimination of Malignant and Benign Breast Masses Using Computer-Aided Diagnosis from Dynamic Contrast-Enhanced Magnetic Resonance Imaging.
Aim: To reduce operator dependency and achieve greater accuracy, the computer-aided diagnosis (CAD) systems are becoming a useful tool for detecting noninvasively and determining tissue characterization in medical images. We aimed to suggest a CAD system in discriminating between benign and malignan...
| Publicado en: | Medical Bulletin of Haseki / Haseki Tip Bulteni Vol. 59; no. 3; pp. 190 - 196 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Jun2021
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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=150718389&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150718389 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13020072 B6K1 jtl: Medical Bulletin of Haseki / Haseki Tip Bulteni issn: 13020072 maglogo: N pubinfo: dt: Jun2021 vid: 59 iid: 3 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 150718389 150718389 150718389 10.4274/haseki.galenos.2021.6819 150718389 ppf: 190 ppct: 6 formats: tig: atl: Discrimination of Malignant and Benign Breast Masses Using Computer-Aided Diagnosis from Dynamic Contrast-Enhanced Magnetic Resonance Imaging. aug: au: Ikizceli, Turkan Karacavus, Seyhan Erbay, Hasan Yurttakal, Ahmet Hasim affil: University of Health Sciences Turkey, Istanbul Haseki Training and Research Hospital, Clinic of Radiology, Istanbul, Turkey sug: subj: Breast Neoplasms Diagnosis Diagnosis, Computer Assisted Utilization Magnetic Resonance Imaging Methods Human Algorithms Utilization Biopsy Decision Trees Discriminant Analysis Descriptive Statistics ab: Aim: To reduce operator dependency and achieve greater accuracy, the computer-aided diagnosis (CAD) systems are becoming a useful tool for detecting noninvasively and determining tissue characterization in medical images. We aimed to suggest a CAD system in discriminating between benign and malignant breast masses. Methods: The dataset was composed of 105 randomly breast magnetic resonance imaging (MRI) including biopsy-proven breast lesions (53 malignant, 52 benign). The expectation-maximization (EM) algorithm was used for image segmentation. 2D-discrete wavelet transform was applied to each region of interests (ROIs). After that, intensity-based statistical and texture matrix-based features were extracted from each of the 105 ROIs. Random Forest algorithm was used for feature selection. The final set of features, by random selection base, splatted into two sets as 80% training set (84 MRI) and 20% test set (21 MRI). Three classification algorithms are such that decision tree (DT, C4.5), naive bayes (NB), and linear discriminant analysis (LDA) were used. The accuracy rates of algorithms were compared. Results: C4.5 algorithm classified 20 patients correctly with a success rate of 95.24%. Only one patient was misclassified. The NB classified 19 patients correctly with a success rate of 90.48%. The LDA Algorithm classified 18 patients correctly with a success rate of 85.71%. Conclusion: The CAD equipped with the EM segmentation and C4.5 DT classification was successfully distinguished as benign and malignant breast tumor on MRI. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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