For screening breast tumors, different imaging modalities like ultrasound, mammography, computed tomography (CT), magnetic resonance imaging (MRI) have been utilized. Mammography and CT use ionizing radiations and hence are not preferred for pregnant women. Even though MRI has high sensitivity for differentiating between breast tumor types, it is costlier and not available everywhere. Therefore, ultrasound is used more prominently for screening of breast tissue due to its ease of use, portability, low cost and safety. Ultrasound images are marred by speckle noise, hence an accurate diagnosis of abnormalities is challenging even for experienced radiologists. Therefore, increasing amount of interest has been observed among researchers to address these limitations and enhance the diagnostic potential of ultrasound images. Accordingly, in the present work, an exhaustive review of machine learning and deep learning based computer aided diagnostic (CAD) system designs has been conducted and brain storming diagrams have been used to indicate the characterization approaches for each stage i.e. (i) datasets, (ii) pre-processing methods, (iii) data augmentation methods, (iv) segmentation methods, (v) feature extraction methods, (vi) feature selection methods, (vii) classification methods and (viii) evaluation metrics. The paper also presents (a) clinically significant sonographic features for differentiating between breast tumor types, (b) achievements made in the design of CAD systems for breast tumor classification and (c) future challenges in designing such systems. The directions for future research to further enhance the diagnostic potential of ultrasound imaging modality for differential diagnosis between different breast abnormalities have also been highlighted.

为了筛查乳腺肿瘤，已经使用了不同的成像方式，如超声、乳房 X 光检查、计算机断层扫描 (CT)、磁共振成像 (MRI)。乳房 X 光检查和 CT 使用电离辐射，因此不适合孕妇。尽管 MRI 在区分乳腺肿瘤类型方面具有很高的敏感性，但它更昂贵且并非随处可用。因此，超声因其易用性、便携性、低成本和安全性而更加突出地用于乳房组织的筛查。超声图像受到散斑噪声的影响，因此即使对于经验丰富的放射科医生来说，准确诊断异常也具有挑战性。因此，研究人员对解决这些局限性和增强超声图像的诊断潜力越来越感兴趣。因此，在目前的工作中，对基于机器学习和深度学习的计算机辅助诊断 (CAD) 系统设计进行了详尽审查，并使用头脑风暴图来指示每个阶段的表征方法，即 (i) 数据集，(ii)预处理方法，(iii) 数据增强方法，(iv) 分割方法，(v) 特征提取方法，(vi) 特征选择方法，(vii) 分类方法和 (viii) 评估指标。该论文还介绍了 (a) 用于区分乳腺肿瘤类型的具有临床意义的超声特征，(b) 在设计用于乳腺肿瘤分类的 CAD 系统方面取得的成就，以及 (c) 设计此类系统的未来挑战。