## 1 Introduction  
The natural performance mechanism of the human body sustains a cycle of revival processes and balances the growth and mortality rates of its cells. However, the abnormal growth of a few cells can cause cancer, which grows and distributes across the human body. According to the [1] report, several types of cancer develop in the human body. For women, breast cancer is one of the leading causes of death. Various factors cause breast cancer: human anatomy (man or woman), family history, age, obesity, and food [2]. However, abnormal cell growth can be classified into two significant case types: benign and malignant cases. Benign cases are considered to be noncancerous or nonlife-threatening, but they occasionally transform into cancer. The human immunity system is known as a \"sac\" [1]; it is a usual mechanism that can separate benign tumors from normal cells and then remove them from the body. For malignant cases, cancer originates from abnormal cell growth that might suddenly distribute or attack neighboring cells. If cells exhibit abnormal growth, the nuclei of the tissue might rapidly become more prominent than normal tissue, which might lead to a frightening case in the future. To identify breast tumor cases, immediately imaging the target area can help a doctor perform further diagnoses. Two groups of medical photography techniques are based on skin perforation and tissue condition: (i) noninvasive methods, i.e., computer-aided tomography (CAT), ultrasound, X-ray, and magnetic resonance imaging (MRI); and (ii) invasive methods, i.e., histopathological imaging (aka. biopsy imaging). A histopathological image is a microscopic tissue image that is widely used for disease analysis and predominates as the gold standard for cancer diagnosis [3,4]. Such images give valuable and essential information that doctors can intensively investigate to find the current circumstances of the patient. Until recently, histopathology images were difficult to discover and retrieve and were unavailable to the scientific community. Therefore, most histopathology image analyses were performed on small datasets, especially for breast cancer images. To attenuate this gap, ref. [5] proposed a dataset called BreaKHis that has become one of the largest and most popular datasets, with 7909 breast histopathology images from 82 patients divided into two main classes: benign and malignant. Figure 1 shows examples of breast histopathology images obtained from this dataset. Feature extraction is an important process used to identify relevant information for any classifier. There are several approaches for feature extraction; Pixel-level features that determine the features from the pixel in the image, Global features that are used to describe the entire image, and Local features that are used only to describe the exciting part of the image. The first-level classification performance of six handcrafted feature vectors was also provided as baselines by [5] to distinguish between benign and malignant cases. These handcrafted features are based on the pixel-level features achieved by state-of-the-art descriptors, including grey-level co-occurrence matrices (GLCMs) [6], local binary patterns (LBPs) [7], threshold adjacency statistics (PFTASs) [8], local phase quantization (LPQ) [9], completed LBPs (CLBPs) [10], and oriented FAST and rotated BRIEF (ORB) [11]. In machine learning (ML), it is one of the most popular approaches to getting computers to act without being explicitly programmed. In many domains, such as natural language processing (NLP) [12, 13, 14], computer vision (CV) [15, 16, 17], and deep learning (DL) [18, 19, 20, 21, 22], numerous problems have been solved with increasing accuracy and dramatic results. Several feature extraction methods are adopted in medical imaging, especially for histopathological image classification. For example, the effectiveness of an ensemble classification method by adopting a set of support vector machines (SVMs) and random subspace ensembles of multilayer perceptrons (MLP) was explored by [23].