# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 2 Materials and Methods
### Feature Extraction  
Image feature extraction plays a crucial role in computer vision by capturing essential information for image analysis and recognition. Feature extraction involves capturing the essential shape details present in a pattern, streamlining the process of categorizing it through a structured method. In the realms of pattern recognition and image processing, feature extraction serves as a specialized method of reducing dimensionality. When dealing with extensive input data that potentially contains redundant information, feature extraction comes into play by transforming these data into a streamlined set of features, also known as a feature vector. By selecting these extracted features, the expectation is to capture the critical information from the input data, facilitating the intended task using this condensed representation rather than the full-scale input. Among the commonly employed methods for feature extraction are Template matching, deformable templates, unitary image transforms, graph description, projection histograms, contour profiles, zoning, geometric moment invariants, Zernike moments, spline curve approximation, Fourier descriptors, gradient features, and Gabor features [17]. In classification systems, feature extraction typically follows the preprocessing phase. The main objective of pattern recognition is to accurately categorize an input pattern into one of the potential output classes. This process generally comprises two main stages: feature selection and classification. Effective feature selection is paramount to the entire process, as a classifier will struggle to discern patterns from inadequately chosen features. Low-order statistical features encompass basic statistical measures derived from pixel intensities, providing fundamental insights into image content [18; 19]. Mean, variance, skewness, and kurtosis are typically basic statistical descriptors offering foundational tools for image characterization [20]. Other features, such as Gabor filters, based on Gabor wavelets, excel in texture analysis by capturing multi-scale and multi-orientation information [17]. The efficacy of Gabor filters in texture representation is crucial for texture-based recognition tasks [21]. Histogram of Oriented Gradients (HOG) descriptors compute gradient orientation histograms across image regions, encapsulating shape and edge information [22]. HOG features excel in object detection tasks, especially in pedestrian detection and recognition [23]. Local binary patterns (LBPs) encode local texture patterns invariant to illumination changes [14]. LBP-based features provide robust representations  
Figure 1: Representation of the eight tissue types, (**1**) tumor epithelium, (**2**) complex stroma, (**3**) immune cells (including immune cell conglomerates and sub-mucosal lymphoid follicles), (**4**) simple stroma (homogeneous composition, including tumor stroma, extra-tumoral stroma, and smooth muscle), (**5**) debris (including necrosis, hemorrhage, and mucus), (**6**) adipose tissue, (**7**) background/empty (no tissue), and (**8**) normal mucosal gland. for texture analysis. Furthermore, RGB color features represent color information in images by decomposing them into three channels, red, green, and blue. While not specifically tied to academic literature, RGB color spaces remain foundational for color-based analysis in image processing [14]. In the current study, we extracted and utilized the features that are presented in Table 1. Feature selection was based on relevancy, and we extracted features that are widely used in the literature and have been suggested in previous studies [14; 17; 20; 21; 22; 23]. Those features were extracted from all the CRC images that were used as our dataset. We also generated a correlation matrix (Figure 2) in order to visualize the dependency between the features that we extracted from the dataset. Most features exhibited small to medium correlations, while some features, especially colors, were highly correlated.