## 1 Introduction
[16] recommended a system to extract grey level co-occurrence matrix (GLCM) features from ranklet BUS images and illustrated that the result is significant in clinical use, but GCM is sensitive to speckle noise and other artifacts in BUS images. [PERSON] et al. [17] suggested phased congruency-based binary pattern (PCBP) that merges the phase congruency (PC) method with that of the local binary pattern (LBP). The features are classified through SVM with the radial basis function (RBF) and showed robustness of the proposed method. The shape-based features very effectively differentiated various breast lesions in some researches [18; 19] and showed that these features are more appropriate for breast tumor detection. The objective of shape-based features is to measure contour and shape characteristics of breast lesions. Typically, ill-defined and irregular boundaries are observed in the case of malignant tumors [9], so the goal of the shape-based features is to evaluate the lesion margin and shape. However, the efficacy of shape-based features relies on a US scanner, the particular view of the lesion, preprocessing technique, and segmentation algorithm [20]. Deep learning is applied in image processing to solve challenging tasks but it needs further improvement to solve all bottlenecks. There are certain bottlenecks where conventional methods with hybrid features demonstrated the capability of a better solution [21]. Recently, deep learning and convolutional neural network (CNN)-based methodologies are employed for benign or malignant lesion recognition [22; 23], but the cost of computation complexity of these methods are a major barriers in clinical applications [24; 25]. To overcome this barrier, researchers have been considering various methods to lessen the time and cost related with deep learning application. The advent of deep learning may launch several ways to do something with conventional methods to overwhelm the various challenges deep learning brings (e.g., time, accuracy, computing power, quantity, and characteristics inputs) [21]. In CADx systems, the main focus is the automatic finding and classification of breast lesion. Imaging modalities supporting the type of texture features [14; 15; 16] and shape-based features [18; 19; 20] have been employed to identify breast cancer ailment. However, it is still a difficult job to choose appropriate features for finding cancer at its early stage [19]. The goal of our research to enhance classification accuracy, and specially:  
1. To develop an automated CADx system to detect breast cancer accurately. 2. To introduce the marker-controlled watershed transformation for efficient segmentation. 3. To extract hybrid feature set incorporating both shape-based and texture features to describe lesions in detail and to overcome the limitations of texture-based methods for BUS images. To give an understanding of the proposed methodology, the rest of this article is structured in the following sections. Section 2 illustrates the framework of the proposed method, implementation procedure, including description of the employed dataset, segmentation technique, and feature extraction method. Section 3 reports the experimental results and their discussion are presented in Section 4. Section 5 concludes the work with future direction.