## 1 Introduction  
Breast cancer (BC) causes cells in the breast to develop uncontrollably, which can lead to tumor growth and death if not detected early. In 2018, an estimated 627,000 womendied from BC, which corresponds to 15% of the total cancer mortality in women [1]. A recent study by the American Cancer Society (ACS) suggests that one in eight women in the US will develop cancer in their lifetime [2]. Globally, BC is a leading type of cancer among women, affecting about 2.1 million women annually, and has been the leading cause of death associated with cancer among women [3]. Early detection and classification of breast cancer subtypes are crucial in deciding the best treatment plan and mitigating the risk of death. According to the World Health Organization (WHO), increasing the survival rates of patients with breast cancer significantly requires early and precise diagnosis of malignancy [4]. There are two kinds of growth in breast tissue, non-harmful (benign) and malignant, with subtypes occurring in each category. Non-harmful (benign) growth patterns include adenosis (A), fibroadenoma (FA), phyllodes tumor (PT), and tubular adenoma (TA), and dangerous (malignant or cancerous) growth patterns include ductal carcinoma (DC), lobular carcinoma (LB), mucinous carcinoma (MC), and papillary carcinoma (PC). These subtypes of BC have distinct biological features, leading to different response patterns to various treatment modalities and thus have varied clinical outcomes. Consequently, to ensure that sufferers receive lifesaving, patient-tailored treatment early, it is very important to accurately distinguish dangerous malignant subtypes of tumors from benign harmless subtypes [5] during patient assessments. Global gene expression profiling (GEP) [6] studies have shown that survival is associated with the classification of distinct biological classes. As a result of limited knowledge and availability of experts, between 10% and 30% of BCs go undetected during regular screenings. The accuracy of manual BC screening varies according to the pathologist's experience and knowledge, and diagnoses can be incorrect due to human error. Automated computer screening systems for breast cancer classification and identification have been proposed to automatically diagnose malignancy, improving the accuracy and consistency of differentiating the normal vs. abnormal classes of breast tissues by about 10% [7]. Computer aided diagnosis (CAD) systems are accessible, fast, and reliable [8]. Machine learning using handcrafted image features was used in previous image-based breast cancer classification studies [9; 10; 11]. Recently, due to their demonstrated impressive performance, deep convolutional neural networks (DCNNs) have become increasingly popular for medical image analysis, segmentation, classification, and aliment prediction [12]. Breast cancer can be detected by automated therapeutic imaging techniques such as histopathological imaging, computed tomography, breast X-rays, sonograms, and magnetic resonance imaging [13]. Currently, histopathological images are considered the best diagnostic images for cancer diagnosis [14]. Several top-down and bottom-up image analyses rely on automated and exact classification of histopathological images, such as classifying nuclei, detecting mitosis, and segmenting glands [15]. Tumor classification, however, is the most critical step in histopathological image examination. A wide range of image analysis tasks can be performed with convolutional neural networks (CNNs), including image classification, disease detection, localization, segmentation [16], and the analyses of histopathological images [12]. Our approach: In this study, a method for multiclass classification of breast cancer using an ensemble of pre-trained deep convolutional neural network (DCNN) and SVM, is proposed. First, four state-of-the-art DCNN backbone models (1) ResNet50 [17], (2) ResNet18 [17], (3) DenseNet201 [18], and (4) EfficientNetb0 [19] were used for feature extraction.