Aim: This study proposes the combination of B-mode and color Doppler mode using Mutual Information including Canonical Correlation Analysis (MI-CCA) to improve breast cancer diagnosis.Materials and methods: The dataset consisted of 53 benign lesions and 202 malignant lesions including B-mode, and color Doppler mode. Convolutional Neuron Networks (CNNs) was applied to automatically extract the features from breast ultrasound images. Then, MI-CCA was performed to fuse with maximized correlation. Finally, the classification model was built via the support vector machine technique to distinguish breast tumors. Diagnosis performances of single modes, combination modes, and other fusion strategies were compared.Results: The single B-mode obtained 90.92% accuracy, while the color Doppler mode obtained 97.16% accuracy. The MI-CCA fusion reveals a significant improvement with 98.80% accuracy. The results indicated that the fusion of two modes tended to offer a more accurate diagnosis than the single mode. In addition, the unsupervised-PCA was high (AUC 0.91, 95% CI [0.90, 0.91]) and no significant difference was observed with the unsupervised-CCA (AUC 0.90, 95% CI [0.84, 0.90]). The supervised-PCA was the lowest (AUC 0.93, 95% CI [0.91, 0.93] and no significant difference was observed with the supervised-CCA (AUC 0.95, 95% CI [0.91, 0.94]). The proposed MI-CCA was the highest performance (AUC 0.99, 95% CI [0.93, 0.99]). These results indicated that the supervised strategies tended to give a more accurate diagnosis than unsupervised strategies.Conclusion: By using the combination of ultrasound modes, this approach achieves high performance compared with the single mode and other fusion strategies. Our methodology may be a beneficial tool for the early detection and diagnosis of breast cancer
Mutual information inspired feature selection using kernel canonical correlation analysis
