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Comput Methods Programs Biomed. 2020 Jan 25;190:105361. doi: 10.1016/j.cmpb.2020.105361. [Epub ahead of print]

Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.

Author information

1
Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea.
2
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC.
3
Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, ROC.
4
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, ROC; Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, Taiwan, ROC; MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan, ROC. Electronic address: rfchang@csie.ntu.edu.tw.

Abstract

Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD software has some problems (such as handcrafted features are hard to design; conventional CAD systems are difficult to confirm overfitting problems, etc.). In our study, we propose a CAD system for tumor diagnosis using an image fusion method combined with different image content representations and ensemble different CNN architectures on US images. The CNN-based method proposed in this study includes VGGNet, ResNet, and DenseNet. In our private dataset, there was a total of 1687 tumors that including 953 benign and 734 malignant tumors. The accuracy, sensitivity, specificity, precision, F1 score and the AUC of the proposed method were 91.10%, 85.14%, 95.77%, 94.03%, 89.36%, and 0.9697 respectively. In the open dataset (BUSI), there was a total of 697 tumors that including 437 benign lesions, 210 malignant tumors, and 133 normal images. The accuracy, sensitivity, specificity, precision, F1 score, and the AUC of the proposed method were 94.62%, 92.31%, 95.60%, 90%, 91.14%, and 0.9711. In conclusion, the results indicated different image content representations that affect the prediction performance of the CAD system, more image information improves the prediction performance, and the tumor shape feature can improve the diagnostic effect.

KEYWORDS:

Breast cancer; Breast ultrasound; Computer-aided diagnosis; Convolutional neural network; Deep learning; Ensemble learning

PMID:
32007839
DOI:
10.1016/j.cmpb.2020.105361

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no financial and personal relationships with other people or organizations that could inappropriately influence their work.

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