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Deep Convolutional Neural Networks (CNN) provides an "end-to-end" solutionfor image pattern recognition with impressive performance in many areas ofapplication including medical imaging. Most CNN models of high performance usehand-crafted network architectures that require expertise in CNNs to utilisetheir potentials. In this paper, we applied the Efficient Neural ArchitectureSearch (ENAS) method to find optimal CNN architectures for classifying breastlesions from ultrasound (US) images. Our empirical study with a dataset of 524US images shows that the optimal models generated by using ENAS achieve anaverage accuracy of 89.3%, surpassing other hand-crafted alternatives.Furthermore, the models are simpler in complexity and more efficient. Our studydemonstrates that the ENAS approach to CNN model design is a promisingdirection for classifying ultrasound images of breast lesions.

An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images