###### Abstract  
Breast ultrasound (BUS) is an effective clinical modality for diagnosing breast abnormalities in women. Deep-learning techniques based on convolutional neural networks (CNN) have been widely used to analyze BUS images. However, the low quality of B-mode images owing to speckle noise and a lack of training datasets makes BUS analysis challenging in clinical applications. In this study, we proposed an end-to-end CNN framework for BUS analysis using multiple parametric images generated from radiofrequency (RF) signals. The entropy and phase images, which represent the microstructural and anatomical information, respectively, and the traditional B-mode images were used as parametric images in the time domain. In addition, the attenuation image, estimated from the frequency domain using RF signals, was used for the spectral features. Because one set of RF signals from one patient produced multiple images as CNN inputs, the proposed framework overcame the limitation of datasets in a broad sense of data augmentation while providing complementary information to compensate for the low quality of the B-mode images. The experimental results showed that the proposed architecture improved the classification accuracy and recall by 5.5% and 11.6%, respectively, compared with the traditional approach using only B-mode images. The proposed framework can be extended to various other parametric images in both the time and frequency domains using deep neural networks to improve its performance. medical ultrasound imaging; breast ultrasound; deep learning techniques; convolutional neural network; quantitative ultrasound; B-mode image; entropy image; phase image; attenuation image +
Footnote †: journal: Medical Imaging  
and malignant classes. However, the low image quality, contrast, and signal-to-noise ratio owing to speckle noise make these visual features in a B-mode image ambiguous and unclear. Therefore, image analysis in the time (or spatial) domain becomes a challenging task for clinical applications. Thus, quantitative ultrasound (QUS) is studied to understand the different aspects of ultrasonic properties in the spectral domain using ultrasound radiofrequency (RF) signals, which are closely related to the frequency characteristics of soft tissue [3,4]. The early-stage CAD systems using machine learning techniques also utilized the abovementioned heuristic and handcrafted features to classify BUS images [5, 6, 7]. Deep convolutional neural networks (CNNs), which can automatically learn various features from images via a training process with many datasets, have been widely utilized in CAD systems. CNN techniques are beneficial for performing two main clinical tasks in BUS: classification and segmentation [8, 9, 10]. However, because the number of datasets in BUS images is limited and their sizes are relatively smaller than the general natural image datasets, convolutional deep learning approaches for medical ultrasound applications have difficulties achieving acceptable performance in real clinical analysis. Data augmentation is used to compensate for the dataset size while avoiding overfitting [11,12]. Although image transformation techniques, such as rotating, resizing, flipping, cropping, and adjusting image contrast, are commonly applied to increase the volume of training data, these artificially inflating data are similar to oversampling in signal processing and unable to provide a solution for the limitation of datasets for BUS images. Further, transfer learning with fine-tuning techniques is also adapted for medical imaging areas to compensate for the shortcomings of datasets [13]. Although many deep neural networks pre-trained with large natural image datasets such as ImageNet [14] achieve acceptable performance for gray-scale BUS images, more accurate object analysis is necessary for real clinical applications.