Combining Nakagami imaging and convolutional neural network for breast lesion classification
Michał Byra, Hanna Piotrzkowska‐Wróblewska, Katarzyna Dobruch‐Sobczak, Andrzej Nowicki · 2017 IEEE International Ultrasonics Symposium (IUS) · 2017
In this paper we propose a computer-aided diagnosis system for the breast lesion classification. Our approach is based on quantitative ultrasound and deep learning. We used the Nakagami imaging to create parametric maps of breast lesions that illustrate tissue scattering properties. For this task the sliding window technique was applied. The Nakagami parameter was calculated using the maximum likelihood estimator. Next, we used the Nakagami parameter maps to train a convolutional neural network. Classification performance was evaluated by 5-fold cross-validation. We obtained the area under the receiver operating characteristic curve equal to 0.91. The results showed that our approach is useful to distinguishing between malignant and benign breast lesions. The proposed method serves as a general approach for tissue characterization and differentiation. The Nakagami parameter used in this study can be replaced with other QUS parameters and the neural network can be trained in a similar fashion.