A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors.

Abstract:

:Breast cancer is one of the leading causes of female cancer deaths. Early diagnosis with prophylactic may improve the patients' prognosis. So far ultrasound (US) imaging is a popular method in breast cancer diagnosis. However, its accuracy is bounded to traditional handcrafted feature methods and expertise. A novel method named Dual-Sampling Convolutional Neural Networks (DSCNN) was proposed in this paper for the differential diagnosis of breast tumors based on US images. Combining traditional convolutional and residual networks, DSCNN prevented gradient disappearance and degradation. The prediction accuracy was increased by the parallel dual-sampling structure, which can effectively extract potential features from US images. Compared with other advanced deep learning methods and traditional handcraftedfeaturemethods,DSCNNreachedthebestperformance withanaccuracyof91.67%andan AUC of 0.939. The robustness of the proposed method was also verified by using a public dataset. Moreover, DSCNN was compared with evaluation from three radiologists utilizing US-BI-RADS lexicon categories for overall breast tumors assessment. The result demonstrated that the prediction sensitivity, specificity and accuracy of the DSCNN were higher than those of the radiologist with 10- year experience, suggesting that the DSCNN has the potential to help doctors make judgement in clinic.

journal_name

Phys Med Biol

authors

Xie J,Song X,Zhang W,Dong Q,Wang Y,Li F,Wan C

doi

10.1088/1361-6560/abc5c7

subject

Has Abstract

pub_date

2020-10-29 00:00:00

eissn

0031-9155

issn

1361-6560

pub_type

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