Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer.
This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 25...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 5; pp. 1218 - 1224 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2020
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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=146532216&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146532216 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2020 vid: 33 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146532216 144159835 146532216 146532216 10.1007/s10278-020-00357-7 146532216 ppf: 1218 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer. aug: au: Zhang, Heqing Han, Lin Chen, Ke Peng, Yulan Lin, Jiangli affil: Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China sug: subj: Breast Neoplasms Ultrasonography Neural Networks (Computer) Prediction Models Computers and Computerization Sensitivity and Specificity Human Retrospective Design ROC Curve Ultrasound Technologists Descriptive Statistics Validity ab: This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corresponding areas under the curve (AUCs) were obtained. The model with the highest AUC was selected, and its diagnostic accuracy was compared with that obtained by sonographers who performed and interpreted ultrasonographic examinations using 683 images of the comparison group (benign: 493; malignant: 190). In the model test with the test group images, the AUCs of the constructed InceptionV3, VGG16, ResNet50, and VGG19 models were 0.905, 0.866, 0.851, and 0.847, respectively. The InceptionV3 model showed the largest AUC, with statistically significant differences compared with the other models (P < 0.05). In the classification of the comparison group images, the AUC (0.913) of the InceptionV3 model was larger than that (0.846) obtained by sonographers, showing a statistically significant difference (P < 0.05). The breast ultrasound computer-aided prediction model based on CNN showed high accuracy in the prediction of breast cancer. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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