Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer.
Afaf F. Moustafa,Theodore W. Cary,Laith R. Sultan,Susan M. Schultz,Emily F. Conant,Santosh S. Venkatesh,Chandra M. Sehgal +6 more
- Vol. 10, Iss: 9, pp 631
TLDR
Quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS).Abstract:
Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.read more
Citations
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Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion
Kiran Jabeen,Muhammad Attique Khan,Majed Alhaisoni,Usman Tariq,Yudong Zhang,Ameer Hamza,Arturas Mickus,Robertas Damaševičius +7 more
TL;DR: A new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features is proposed, which outperforms recent techniques.
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