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Computer-aided detection (CAD) of benign and malignant breast lesions becomesincreasingly essential in breast ultrasound (US) imaging. The CAD systems relyon imaging features identified by the medical experts for their performance,whereas deep learning (DL) methods automatically extract features from thedata. The challenge of the DL is the insufficiency of breast US imagesavailable to train the DL models. Here, we present an ensemble transferlearning model to classify benign and malignant breast tumors using B-modebreast US (B-US) and strain elastography breast US (SE-US) images. This modelcombines semantic features from AlexNet & ResNet models to classify benign frommalignant tumors. We use both B-US and SE-US images to train the model andclassify the tumors. We retrospectively gathered 85 patients' data, with 42benign and 43 malignant cases confirmed with the biopsy. Each patient hadmultiple B-US and their corresponding SE-US images, and the total datasetcontained 261 B-US images and 261 SE-US images. Experimental results show thatour ensemble model achieves a sensitivity of 88.89% and specificity of 91.10%.These diagnostic performances of the proposed method are equivalent to orbetter than manual identification. Thus, our proposed ensemble learning methodwould facilitate detecting early breast cancer, reliably improving patientcare.

Ensemble Transfer Learning of Elastography and B-mode Breast Ultrasound Images