Abstract
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.
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Acknowledgments
We gratefully acknowledge the kind cooperation of Haihong Intellimage Medical Technology (Tianjin) Co., Ltd., in terms of software and technical service.
Funding
This work was supported by the Achievement Conversion and Guidance Project of Chengdu Science and Technology Bureau (No.2017-CY02-00027-GX).
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Zhang, H., Han, L., Chen, K. et al. Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer. J Digit Imaging 33, 1218–1223 (2020). https://doi.org/10.1007/s10278-020-00357-7
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DOI: https://doi.org/10.1007/s10278-020-00357-7