Deep learning-based classification of breast ultrasound with parametric quantitative images and RF beamsum data

Ultrasound is used as a supplementary diagnostic tool to diagnose breast cancer after initial screening mammography, due to its high false positive rate. To improve upon the false positive rate, computer aided diagnostic systems have been introduced that analyze grayscale images. However, the conversion to grayscale results in a significant loss of acoustic information. Therefore, this study explores the novel fusion of seven different raw quantitative ultrasound features extracted from the raw data to classify breast ultrasound images. With these added features, our algorithm shows improved accuracy by 15.62% compared to grayscale alone, which is promising to improve breast ultrasound.