Breast Tumor Detection in Ultrasound Images Using Deep Learning

Detecting tumor regions in breast ultrasound images has always been an interesting topic. Due to the complex structure of breasts and the existence of noise in the ultrasound images, traditional handcraft feature based methods usually cannot achieve satisfactory results. With the recent advance of deep learning, the performance of object detection has been boosted to a great extent, especially for general object detection. In this paper, we aim to systematically evaluate the performance of several existing state-of-the-art object detection methods for breast tumor detection. To achieve that, we have collected a new dataset consisting of 579 benign and 464 malignant lesion cases with the corresponding ultrasound images manually annotated by experienced clinicians. Comprehensive experimental results clearly show that the recently proposed convolutional neural network based method, Single Shot MultiBox Detector (SSD), outperforms other methods in terms of both precision and recall.