## 1 Introduction  
Breast cancer is one of the most prevalent cancer types, with 2.3 million women diagnosed with this cancer and 685,000 deaths globally in 2020 [1]. For this large amount of patients, medication should be accurate with little to zero margin for error; otherwise, the consequences of a wrong diagnosis could be fatal [2]. Also, since breast cancer is one of the leading causes of death for women globally, precise detection and timely treatment can enhance the chances of recovery [3]. Computer-aided diagnosis (CAD) provides some deep learning (DL)-based techniques in digital pathology that can assist pathologists to make more accurate cancer diagnoses [4]. These techniques need to be trained by medical images such as those from magnetic resonance imaging (MRI) [5] and histopathological images [6]. A unique feature of histopathological images is that they are typically much larger than other medical images [7]. Histopathological images play a crucial role in the realm of medical image processing, allowing the integration of image information and pathologists' expertise to improve diagnosis [8]. Medical images have witnessed a rapid expansion in quantity, content, and dimensions [9]. Due to an enormous increase in the number of diverse clinical exams and the availability of a wide range of image modalities, the demand for efficient medical image data retrieval and management has increased [10]. Current approaches to medical image retrieval often rely on alphanumeric keywords assigned by human experts, enabling retrieval at a conceptual level. However, this text-based search methodology falls short in capturing the intricate visual features inherent in image content [11]. Recent Content-Based Histopathological Image Retrieval (CBHIR) methods support full retrieval via visual content and histopathological patterns of the tissue [12]. These advanced CBHIR tools facilitate searching at a perceptual level [13]. It is noteworthy to mention that a single pathology image may contain just basic patterns of tissue such as the epithelium and connective tissue. However, the actual number of patterns in the DL-based technique's point of view is almost infinite. CBHIR explores a database to find visually similar images to provide clinicians with comparable lesions. In the diagnostic workflow, pathologists utilize this search engine to reach top K similar to their queries to determine if a histological feature is malignant or benign [14]. Mainly, CBHIR tools work on the extracted features of the images, including color, texture, shape, etc. Color is a visual feature that plays an important role in CBHIR techniques due to its invariance with respect to image scaling, translation, and rotation [15]. The use of color improves the capturing of distinctive histopathological features. This provides valuable information about the distribution and arrangement of different tissue components in histopathology [16]. In histopathology, tissues must be stained using various dyes, including hematoxylin and eosin (H&E), in order to be readable for pathologists [9; 17]. In digital pathology, these tissues must be scanned as whole-slide images (WSIs) [18]. In addition to the use of different scanners and staining manufacturers, lab conditions and temperatures may cause color variation in WSIs [19]. Color variation in WSIs can arise from both inter- or/and intra-laboratory factors in the acquisition procedure [16]. Figure 1 shows the color variation across five different collaborating hospitals involved in the collection of the CAMELYON17 challenge (CAM17) data set [20]. This diverse color variation misleads the model and potentially deceives the feature extractors (FEs) into extracting erroneous features. Also, the performance of CAD tools may be significantly influenced by these variations in WSIs [21]. In this paper, we propose a CBHIR framework that includes color normalization (CN) as a pre-processing step to address this issue.