# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories
## 2 Related Work  
Various Computer-Aided-Diagnosis (CAD) systems in the breast imaging modality has been widely developed. In general, traditional CAD systems consist of two major stages: one is segmentation of masses, and the other is severity classification of masses. Some works also explored segmentation of breast masses. For example, [PERSON] et al. [2] used semi-automatic CAD system to detect the contours of breast lesion. [PERSON] [3] developed a fully automatic detection system for breast lesion segmentation. Both accurate lesion boundary detection and feature selection are important for breast cancer diagnosis. The research in [2] identifies lesion severity on ultrasound images using morphology features, and the researches in [3; 4; 5] adopt texture features for classification. Both of morphology features and texture features are used for classification [6; 7; 8]. [PERSON] et al [7] cooperates with pathological diagnosis to identify a breast lesion with BI-RADS as benign or malignant. As a whole, most of the researches only can predict the detected breast lesion tumor as benign and malignant [2; 3; 4; 5; 6; 7; 8]. In recent years, most of researches who conduct classification of breast ultrasound BI-RADS only focused on borderline category such as BI-RADS 3 to reduce the ambiguities. Related studies and their adopted methods are illustrated in Table 3, respectively. In actuality, there exist several speckle noises on BUS images as a result of human factors or mechanical imaging factors. Traditional noise removal method cannot reduce the speckle noises effectively. Therefore, [PERSON] and [PERSON] [9] developed a nonlinear anisotropic diffusion technique, speckle reducing anisotropic diffusion (SRAD) to remove the speckle. Besides, how to extract robust features related to the BI-RADS description is an important issue for accurate classification. [PERSON] [3] adopted region growing from the seed point, speckle reduction, and the Neutrosophic L-Means clustering (NLM) to develop a fully automatic segmentation method for BUS images. Although the segmentation accuracy reaches 94%, this method has two major limitations. The first one is over-segmentation problem occurred where the lesion position near the image boundary. Another problem is that it cannot detect multiple lesions in one image. In this work, we propose a method to improve the problems mentioned above. In recent years, some studies usually capitalized on the machine learning such as the selected features and Support Vector Machine (SVM) to predict the lesion severity with a two-category classification. Recently, Random Forest (RF) has become a commonly used method for multi-class identification especially in surface-types analysis of satellite imagery [10]. Since 2012, [PERSON] [11] proposed Convolution Neural Network (CNN) on color images to identify objects of interest. Classification researches in medical images have gradually aimed at the approaches based on deep learning. In recent years, CNN has been applied and demonstrated to tissue identification of brain MRI images effectively [12; 13], because it can achieve better classification results. Nowadays, [PERSON] et al. [14] using CNN for breast lesions detection; [PERSON] et al. [15] adopted 3-D CNN for breast tumor detection. After further exploration, we ensemble the proposed methods and existing techniques into our proposed breast ultrasound CAD system so that our method can extract more robust features. The features selected by the proposed system are further illustrated in Table 4.