## 2 Related Work  
In problems with histopathologic images, it is increasingly common to use some form of preprocessing. In [38], the authors provide a detailed overview of graph-based deep learning for computational histopathology. They note that stain normalization is a common practice when using standard entity graph pathology workflows. Similarly, in [39] stain normalization is used as preprocessing for a classification model that predicts not only histological sub-types of endometrial carcinoma, but also molecular sub-types and 18 common genetic mutations based on histopathological images.  
A tumor segmentation problem in lung cancer images is addressed in [40]. In the preprocessing stage, they employ a staining normalization to homogenize the images. Then, they apply another type of Gaussian filter as a color augmentation method to make the models used more robust.  
A problem of binary classification of breast cancer images, obtaining an accuracy higher than 98%, is presented in [41]. The procedure the authors follow for its resolution consists of three stages: preprocessing, segmentation and classification. In the preprocessing stage, the authors use a fuzzy equalization of the histogram of the images to overcome unwanted over-enhancement and noise amplification by preserving local information of the original image.  
The segmentation of tumors in Cervical Cancer images was analyzed in [42]. To improve the quality of the images and thus increase the performance of the models, the development of an automatic framework to preprocess the images was proposed. This framework would include different preprocessing methods such as adaptive histogram equalization, Gaussian filters, or sharpen filters among others.  
In [43], the authors propose a study in which they compare the results of applying different filters to retinal fundus images. The filters used are the following: Contrast Limited Adapative Histogram Equalization (CLAHE), Adapative Histogram Equalization (AHE), Median filter, Gaussian filter, Weiner filter, and Adaptive median filters. They also present the advantages and disadvantages of each method, giving a brief explanation of them. To quantify the efficiency of each filter they use the MSE and PSNR measures. Finally, they conclude that the best-performing filter is the adaptive median filter due to its high PSNR and low MSE.  
Regarding the different models of deep learning, the alternatives that are frequently found in the literature and that have been successfully applied in histopathology images are VGG16, VGG19, ResNet50, MobilNet, DenseNet121, among others [44; 45; 46]. For example, in [47], the authors propose a cascaded deep learning framework for accurate nuclei segmentation in digitized microscopy images of histology slides, based on the VGG16 model and a soft-die loss function. In [48] a multi-class classification, using the 6B-Net model selected vector and ResNet50 selected vector was fused and applied to breast cancer. In [44], a review of the popular deep learning algorithms, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and graph neural networks (GNNs) is presented, highlighting the applications in pathology.