Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.
Purpose: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.Materials and Methods: We retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. D...
| Publicado en: | Japanese Journal of Radiology Vol. 37; no. 6; pp. 466 - 473 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2019
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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=136731396&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136731396 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18671071 AUCM jtl: Japanese Journal of Radiology issn: 18671071 maglogo: N pubinfo: dt: Jun2019 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136731396 136731396 NLM30888570 10.1007/s11604-019-00831-5 NLM30888570 136731396 ppf: 466 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. aug: au: Fujioka, Tomoyuki Kubota, Kazunori Mori, Mio Kikuchi, Yuka Katsuta, Leona Kasahara, Mai Oda, Goshi Ishiba, Toshiyuki Nakagawa, Tsuyoshi Tateishi, Ukihide affil: Department of Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, 113-8510, Tokyo, Japan sug: subj: Ultrasonography Methods Image Interpretation, Computer Assisted Methods Breast Neoplasms Middle Age Adult Reproducibility of Results Diagnosis, Differential Sensitivity and Specificity Male Resource Databases ROC Curve Young Adult Breast Aged Aged, 80 and Over Retrospective Design Female Neural Networks (Computer) Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years Aged, 80 & over Male Female ab: Purpose: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.Materials and Methods: We retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.Results: The CNN model and radiologists had a sensitivity of 0.958 and 0.583-0.917, specificity of 0.925 and 0.604-0.771, and accuracy of 0.925 and 0.658-0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728-0.845, p = 0.01-0.14).Conclusion: Deep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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