BCDNet: An Optimized Deep Network for Ultrasound Breast Cancer Detection

Objectives: Breast cancer is a common but deadly disease among women. Medical imaging is an effective method to diagnose breast cancer, but manual image screening is time-consuming. In this study, a novel computer-aided diagnosis system for breast cancer detection called BCDNet is proposed. Material and Methods: We leverage pre-trained convolutional neural networks (CNNs) for representation learning and propose an adaptive backbone selection algorithm to obtain the best CNN model. An extreme learning machine serves as the classifier in the BCDNet, and a bat algorithm with chaotic maps is put forward to further optimize the parameters in the classifiers. A public ultrasound image dataset is used in the experiments based on 5-fold cross-validation. Results: Simulation results suggest that our BCDNet outperforms several state-of-the-art breast cancer detection methods in terms of accuracy. Conclusion: The proposed BCDNet is a useful auxiliary tool that can be applied in clinical screening for breast cancer.