## 2 Related Work  
Several studies have investigated methods for CRC detection, classification, and tissue segmentation by analysis of WSI. [PERSON] et al. [11] presented a new dataset of 5000 histological images of human colorectal cancer that included eight different tissue types. Ten anonymized H&E-stained CRC tissue slides were obtained from the pathology archive at the University Medical Center Mannheim (Heidelberg University, Mannheim, Germany). Contiguous tissue areas were manually labeled and tesselated, resulting in 625 non-overlapping tissue tiles of size 150 \(\times\) 150 pixels. The following eight tissue types were selected for analysis: tumor epithelium, simple stroma, complex stroma, immune cells, debris, normal mucosal glands, adipose tissue, and background (the resulting 625 \(\times\) 8 = 5000 images together formed the training and testing set for the classification problem). The authors used four classification strategies (1-nearest neighbor, linear SVM, radial basis function SVM and decision trees) and found that the radial basis function (rbf) support vector machine (SVM) performed the best (87.4% accuracy for multiclass tissue separation). [PERSON] et al. [12] proposed a CRC tissue classification system based on convolutional nets (ConvNets). They used data from two different sources, namely a cohort of images of whole slides of rectal cancer samples (a set of 74 histological slides from 74 patients), and a dataset of colorectal cancer images and patches (from 10 patients, using 5000 patches of 150 \(\times\) 150 pixel, the dataset of [11]). They investigated the importance of staining normalization (applying staining normalization to training and test data removes most sources of variability due to staining from the equation) in classifying CRC tissues in H&E stained images and achieved 79.7% accuracy. [PERSON] et al. [13] used several data sets for experimental analysis of a novel method called IOCLBP, which is based on a simple-to-implement yet highly discriminative local descriptor for color images. The authors have demonstrated the superiority of IOCLBP alone and/or in combination with LCC over related methods (LBP variants) for the classification of binary and multiclass problems. One such problem is the so-called Epistroma: histological images of colorectal cancer from 643 patients admitted to Helsinki University Central Hospital, Finland, between 1989 and 1998. The tissue samples were stained with diaminobenzzidine and hematoxylin and divided into two classes: Epithelium (825 samples) and Stroma (551 samples). The size of the images varied from 172 \(\times\) 172 pixels to 2372 \(\times\) 2372 pixels. In this binary classification problem of colorectal cancer, the accuracy achieved was 93.4%. [PERSON] et al. [14] used different deep learning models (SqueezeNet, MobileNet, ResNet, and DenseNet) to present two different approaches: (1) generating features from pre-trained models (i.e., without fine-tuning); and (2) fine-tuning the CNN from pre-trained models. The second approach was effective and provided better classification results. When training a SVM on deep features, the authors applied ILFS to obtain a reduced subspace of features while achieving high accuracy. The authors used different problems or datasets to analyze the results, including the data from [PERSON] et al. [11] and that from Epistroma (previously discussed in [PERSON] et al. [13]). Based on SVM classification using the best-scoring deep features from different pre-trained models, the best results obtained for the Kather problem, with 1000 features, were an accuracy of 95.4% and an AUC of 0.906. For the Epistroma problem (simpler since it is biclass ), an accuracy of 99.06% and an AUC of 0.997 were obtained with 250 features, using DenseNet in both datasets. However, neither study worked with a data set of nine different CRC tissue classes (presented in [15] and used in this paper).