Combination of B-mode and color Doppler mode using mutual information including canonical correlation analysis for breast cancer diagnosis.
Aim: This study proposes the combination of B-mode and color Doppler mode using Mutual Information including Canonical Correlation Analysis (MI-CCA) to improve breast cancer diagnosis.Materials and Methods: The dataset consisted of 53 benign lesions and 202 malignant lesions including B-mode, and co...
| Publicado en: | Medical Ultrasonography Vol. 22; no. 1; pp. 51 - 60 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Romanian Society of Ultrasonography in Medicine & Biology
Mar2020
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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=141961023&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141961023 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18444172 AX1S jtl: Medical Ultrasonography issn: 18444172 maglogo: N pubinfo: dt: Mar2020 vid: 22 iid: 1 pid: 57537 pub: Romanian Society of Ultrasonography in Medicine & Biology artinfo: ui: 141961023 141961023 NLM32096788 141961023 10.11152/mu-2270 NLM32096788 141961023 ppf: 51 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Combination of B-mode and color Doppler mode using mutual information including canonical correlation analysis for breast cancer diagnosis. aug: au: Tongjai Yampaka Chongstitvatana, Prabhas Yampaca, Tongjai affil: Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand sug: subj: Breast Neoplasms Diagnostic Imaging Ultrasonography, Doppler Female Ultrasonography, Doppler, Color Human Female ab: Aim: This study proposes the combination of B-mode and color Doppler mode using Mutual Information including Canonical Correlation Analysis (MI-CCA) to improve breast cancer diagnosis.Materials and Methods: The dataset consisted of 53 benign lesions and 202 malignant lesions including B-mode, and color Doppler mode. Convolutional Neuron Networks (CNNs) was applied to automatically extract the features from breast ultrasound images. Then, MI-CCA was performed to fuse with maximized correlation. Finally, the classification model was built via the support vector machine technique to distinguish breast tumors. Diagnosis performances of single modes, combination modes, and other fusion strategies were compared.Results: The single B-mode obtained 90.92% accuracy, while the color Doppler mode obtained 97.16% accuracy. The MI-CCA fusion reveals a significant improvement with 98.80% accuracy. The results indicated that the fusion of two modes tended to offer a more accurate diagnosis than the single mode. In addition, the unsupervised-PCA was high (AUC 0.91, 95% CI [0.90, 0.91]) and no significant difference was observed with the unsupervised-CCA (AUC 0.90, 95% CI [0.84, 0.90]). The supervised-PCA was the lowest (AUC 0.93, 95% CI [0.91, 0.93] and no significant difference was observed with the supervised-CCA (AUC 0.95, 95% CI [0.91, 0.94]). The proposed MI-CCA was the highest performance (AUC 0.99, 95% CI [0.93, 0.99]). These results indicated that the supervised strategies tended to give a more accurate diagnosis than unsupervised strategies.Conclusion: By using the combination of ultrasound modes, this approach achieves high performance compared with the single mode and other fusion strategies. Our methodology may be a beneficial tool for the early detection and diagnosis of breast cancer. 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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