  Computer-aided detection (CAD) of benign and malignant breast lesions becomes
increasingly essential in breast ultrasound (US) imaging. The CAD systems rely
on imaging features identified by the medical experts for their performance,
whereas deep learning (DL) methods automatically extract features from the
data. The challenge of the DL is the insufficiency of breast US images
available to train the DL models. Here, we present an ensemble transfer
learning model to classify benign and malignant breast tumors using B-mode
breast US (B-US) and strain elastography breast US (SE-US) images. This model
combines semantic features from AlexNet & ResNet models to classify benign from
malignant tumors. We use both B-US and SE-US images to train the model and
classify the tumors. We retrospectively gathered 85 patients' data, with 42
benign and 43 malignant cases confirmed with the biopsy. Each patient had
multiple B-US and their corresponding SE-US images, and the total dataset
contained 261 B-US images and 261 SE-US images. Experimental results show that
our ensemble model achieves a sensitivity of 88.89% and specificity of 91.10%.
These diagnostic performances of the proposed method are equivalent to or
better than manual identification. Thus, our proposed ensemble learning method
would facilitate detecting early breast cancer, reliably improving patient
care.
