**2. Related Work**

Many decision-support technologies based on machine learning and deep learning have been included into experimental investigations devoted to the diagnosis of various kinds of tumors, including breast tumors.

A comparative analysis of watershed, mean shift, and k-means segmentation algorithms to detect microcalcifications on breast images belonging to the MIAS database was reported in [17]. The best results were obtained by using k-means segmentation, which detected 42.8% of breast images correctly and had 57.2% false detections.

Zhang et al. [18] introduced the Hu moment invariant as a feature descriptor to diagnose breast cancer. They used k-fold cross-validation to improve the accuracy of the proposed method and to reduce the difficulty of diagnosis.

A watershed segmentation and k-NN (as a supervised learning method) classification were implemented to detect the tumors in the mammogram images and establish the risk of cancer classification [19]. The breast image classification was performed with an 83.33% overall accuracy rate.

Sadhukhan et al. [20] reported a model used to predict the best features for early breast cancer cell identification. The k-NN and support vector machine (SVM) algorithms were studied in terms of accuracy. On the basis of the contours of the cells, the nuclei were distinctly separated, and an accuracy of 97.49% was determined.

Hao et al. [21] used three-channel features of 10 descriptors to improve the accuracy of benign and malignant breast cancer recognition. An SVM algorithm was used to assess the model's performance. A recognition accuracy of 90.2–94.97% at the image level was reported for the model based on texture features, geometric moments, and wavelet decomposition.

Another study [22] proposed a model which used three-channel features of 10 descriptors for the recognition of breast cancer histopathological images, as well as an SVM to improve the classification of benign and malignant breast cancer. The proposed model showed an increase in the recognition time and little improvement in the recognition accuracy.

Joshi and Mehta [23] analyzed the diagnosis accuracy of the k-NN algorithm using a set of 32 features and with and without dimensionality reduction techniques. The reported results showed 97.06% accuracy for benign vs. malignant classification using k-NN with the linear discriminant analysis technique.

Alshammari et al. [24] extracted intensity-based features (such as average intensity, standard deviation, and contrast between the foreground and background), shape-based features (i.e., diameter, length, degree of circulation, and elongation), or texture-based features for classification purpose. The decision tree, k-NN, SVM, naïve Bayes, and discriminant analysis algorithms were used to maximize the separation between the given groups of data and to produce higher-accuracy results.

Agaba et al. [25] used some handcrafted features such as Hu's moment, Haralick textures, color histogram, and a deep neural network for a multiclassification task devoted to breast cancer classification using histopathological images on the BreakHis dataset. They also used various enlargements for histopathological images analysis. An accuracy score of 97.87% was reported for 40× magnification.

Xie et al. [26] used a combination of SVM and extreme learning machine (ELM) to differentiate between malignant and benign masses in mammographic images. The proposed algorithm included mass segmentation based on the level set method, feature extraction, feature selection, and mass type classification. An average accuracy of 96.02% was reported.

Zhuang et al. [27] proposed a method based on multiple features and support vector machines for the diagnosis of breast tumors in ultrasound images. Their algorithm used both characteristic features and deep learning features and a support vector machine for BUS classification. Hu's moment invariants [13] were used to investigate the characteristics of the posterior shadowing region that were different for benign and malignant tumors. The reported results were as follows: accuracy, sensitivity, specificity, and F1-score of 92.5%, 90.5%, 95%, 90.5%, and 92.7%, respectively, showing superiority to other known methods.

Deep features have played an important role in the progress of deep learning. These features are extracted from the deep convolutional layers of pretrained CNNs or customdesigned CNNs. Shia et al. [28] used transfer learning on a pretrained CNN and trained it using a BUS dataset containing 2099 images with benign and malignant tumors. An SVM with a sequential optimization solver was used for classification. A sensitivity of 94.34% and a specificity of 93.22% were reported for the classification.

Wan et al. [29] used content-based radiomic features to analyze BUS images. This study considered 895 BUS images and three radiomic features (i.e., color histogram, Haralick's texture features, and Hu's moments). The classification was performed using seven wellknown machine learning algorithms and a CNN architecture. The best performance in differentiation between benign and malignant BUS was reported for the random forest classifier. The obtained accuracy, sensitivity, specificity, F1-score, and average precision were as follows: 90%, 71%, 100%, 83%, and 90%, respectively.

Moldovanu et al. [30] studied and classified skin lesions using a k-NN-CV algorithm and an RBF neural network. The RBF neural network classifier provided an accuracy of 95.42% in the classification of skin cancer, significantly better than the k-NN algorithm.

Damian et al. [31] used Hu's invariant moments to determine their relevance in the differentiation between nevi and melanomas. The reported results indicated that Hu's moments were good descriptors as they provided a good classification between malignant melanoma and benign lesions.

### **3. Materials and Method**

#### *3.1. Dataset*

All images used in this study belonged to the publicly available digital BUSI (breast ultrasound image) database. The images were in a PNG file format with an average image size of 500 × 500 pixels and an 8 bit gray level. The considered dataset contained 780 images classified into normal images (*n* = 133), images with benign lesions (*n* = 437), and images with malignant lesions (*n* = 210). The images were captured at the Baheya Hospital for Early Detection and Treatment of Women's Cancer, Cairo, Egypt [32]. Experts of ultrasonic imaging evaluated their geometry, density, and internal echo contrast levels. Furthermore, ground-truth images were available. The ground-truth images were generated using the MATLAB programming environment and a freehand segmentation method utilized by radiologists and computer science experts (Figure 1).

**Figure 1.** *Cont*.

**Figure 1.** Samples of breast ultrasound images: (**a**,**c**) grayscale image of a benign lesion; (**b**,**d**) ground truth for benign lesion; (**e**,**g**) grayscale image of a malignant lesion; (**f**,**h**) ground truth for malignant lesion [32].
