## 2 Materials and Methods  
### Datasets  
A total of 996 clinical cases in 1060 US images are used in this study; 250 were from Database-I (Mendeley Dataset), 163 were from Database-II (Dataset UDIAT), and 647 were from Database-III (Dataset BUSI). The Database-I is contributed by [PERSON] et al. [26], available at ([[https://data.mendeley.com/datasets/wmy84](https://data.mendeley.com/datasets/wmy84) gzngow/1]([https://data.mendeley.com/datasets/wmy84](https://data.mendeley.com/datasets/wmy84) gzngow/1); accessed on 6 January 2018). In this database, there are 250 US images of which 100 are fibroadenoma (benign), and 150 are malignant cases. All the images are stored in *.bmp format. The Database-II consists of 163 US images that are stored in *.png format, available at ([[http://www2.docm.mmu.ac.uk/STAFF/m.yap/dataset.php](http://www2.docm.mmu.ac.uk/STAFF/m.yap/dataset.php)]([http://www2.docm.mmu.ac.uk/STAFF/m.yap/dataset.php](http://www2.docm.mmu.ac.uk/STAFF/m.yap/dataset.php)); accessed on 7 January 2018) [27]. In this database, the lesion regions (i.e., tumor contours) of the 163 clinical cases were identified by a radiologist and stored in binary image format in a separate folder while the B-mode US images were stored in another folder. The pathological findings of these 163 lesions were categorized into fibroadenoma (FA), invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS), papilloma (PAP), unknown (UNK), lymph node (LN), lymphoma (LP), etc. Among them, 110 are benign, and 53 are malignant cases. The Database-III consists of 780 US images that are stored in *.png format, available at ([[https://scholar.cu.edu.eg/?q=afahmy/pages/dataset](https://scholar.cu.edu.eg/?q=afahmy/pages/dataset)]([https://scholar.cu.edu.eg/?q=afahmy/pages/dataset](https://scholar.cu.edu.eg/?q=afahmy/pages/dataset)); accessed on 28 February 2021) [28]. This database contains breast ultrasound images at baseline including those of women between 25 and 75 years old; the number of female patients is 600. The dataset consists of 780 images of which 437 are benign, 210 are malignant, and 133 are normal cases. The binary mask images are provided with the corresponding B-mode images. The details of the three datasets are provided in Table 1. For classification purposes, only the benign and malignant cases (i.e., 647 images out of 780 images) are considered in this study from this database. Deep neural networks in general require large computational resources. Applying augmentation, each of the three databases consisted of 1000 benign and 1000 malignant cases. Only the translational augmentation of [\(-\)11 to +11] pixels on both directions was performed on the base images, as any kind of rotation or scaling would also ruin the size or orientation-based features. The overall number of augmented images was then 6000 with 2000 images per database. The primary motivations behind the data augmentation to an equal number of benign and malignant cases were to increase the number of samples necessary for training the neural network as well as to remove the class imbalance. The images in the datasets had already been pre-processed (i.e., speckle reduction, edge enhancement, compressed dynamic range, persistence, etc.) as is typical of clinical scanner outputs. Therefore, there is no need for further pre-processes for removing various noises, artifacts, and anomalies. The necessary steps for preparing the images for the featured-based approach and the CNN-based approach are described in the following sub-sections.