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