## 1 Introduction  
In digital pathology, whole slide images (WSI) are considered the golden standard for primary diagnosis [1; 2]. The use of computer-assisted image analysis tools is becoming mainstream for automatic quantitative or semi-quantitative analysis for pathologists [3; 4; 5; 6; 7]. Cancer is one of the critical public health issues around the world. According to the Global Burden of Disease (GBD) study, there have been more than 25 million cancer incidences and 10 million cancer deaths worldwide in 2020. These statistics indicate that cancer incidence expanded by 33% between 2007 and 2017 worldwide [8] and, for example, is projected to increase more than 92% in 2020 and 2040 in sub-Saharan Africa [9]. Breast cancer is one of the leading causes of cancer-related deaths worldwide and affects a large number of women today [10; 11]. Invasive ductal carcinoma (IDC) is the most frequent subtype of all breast cancers [12]. As stated in [13], 934,870 new cancer cases and 287,270 cancer deaths in women are projected to occur in the United States in 2022, with an estimated 287,850 new cases of breast cancer (the leading cause, accounting for 31% of new cancer cases) and 43,250 deaths from breast cancer (the second leading estimated cause of cancer death, with breast cancer accounting for 15% in women). Incidence rates for breast cancer in women have slowly increased by about 0.5% per year since the mid-2000s [14]. Most women diagnosed with breast cancer are over the age of 50, but younger women can also develop breast cancer. About 1 in 8 women will be diagnosed with breast cancer during their lifetime. Survival rates for breast cancer are believed to have increased and the number of related deaths continues to decline, largely due to factors such as earlier detection, a new personalized treatment approach, and a better understanding of the disease [15; 16]. Histopathology is a technique frequently used to diagnose tumors. It can identify the characteristics of how cancer looks under the microscope. Accurate detection and classification of some types of cancer is a critical task in medical imaging due to the complexity of those tissues [17; 18], concretely in breast cancer [19], which is the one that will be analyzed in this paper. The diagnosis of breast cancer histopathology images with hematoxylin and eosin stained is non-trivial, labor-intensive, and often leads to a disagreement between pathologists [20; 21]. Computer-assisted diagnosis systems contribute to helping pathologists improve diagnostic consistency and efficiency, especially in breast cancer using histopathological images [22; 23; 24]. The use of artificial intelligence tools, especially deep learning methods, to classify histopathological images of patients with breast cancer has been extensively and fruitfully analyzed in the literature [25; 26; 27; 28; 29]. In particular, convolutional neural networks (CNNs) are the most commonly used for histology image analysis tasks [30; 31; 32; 33]. Once the database of the WSI images is available, a phase that is usually performed in different papers presented in the bibliography, is to carry out a pre-processing of said images. The main objective of this pre-processing step is to improve the behavior of the system (several alternatives for pre-processing can be used, among which it is worth mentioning to remove noise, blurring, enhance contrast, and, ultimately, improve the quality of the images to obtain better performance for the final deep learning algorithms). In Figure 1 a typical classification model pipeline is presented in which common techniques, such as data slipt, image pre-processing, tiling the whole slide images (WSI), and statistical evaluation with a test set, are highlighted. This paper proposes a comparative study of how different histopathological image preprocessing methods, and various deep learning models, affect the performance of the classification system in breast cancer. The methods used are divided into two groups: based on convolution filters and based on histogram equalization.