  Breast cancer is the most common cancer in the world and the most prevalent
cause of death among women worldwide. Nevertheless, it is also one of the most
treatable malignancies if detected early. In this paper, a deep convolutional
neural network-based algorithm is proposed to aid in accurately identifying
breast cancer from ultrasonic images. In this algorithm, several neural
networks are fused in a parallel architecture to perform the classification
process and the voting criteria are applied in the final classification
decision between the candidate object classes where the output of each neural
network is representing a single vote. Several experiments were conducted on
the breast ultrasound dataset consisting of 537 Benign, 360 malignant, and 133
normal images. These experiments show an optimistic result and a capability of
the proposed model to outperform many state-of-the-art algorithms on several
measures. Using k-fold cross-validation and a bagging classifier ensemble, we
achieved an accuracy of 99.5% and a sensitivity of 99.6%.
