An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images
This work addresses the need for automated and efficient deep learning model design in medical imaging for breast cancer recognition, though it is incremental as it applies an existing ENAS method to a specific domain.
The paper tackled the problem of classifying breast lesions from ultrasound images by applying Efficient Neural Architecture Search (ENAS) to find optimal CNN architectures, achieving an average accuracy of 89.3% on a dataset of 524 images, surpassing hand-crafted models while being simpler and more efficient.
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.