# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 1 Introduction
The experimental results demonstrated that deep learning methods surpass hand-crafted feature-based methods, and the algorithms proposed outperformed CNN architectures and hand-crafted feature-based methods, resulting in an accuracy of approximately 96%; however, the resulting models were highly complex. Other studies have utilized feature extraction to obtain appropriate features that would ultimately lead to better classification. One study employed machine learning, features engineering, and image processing techniques, as well as five models, XGBoost, RF, SVM, MLP, and LDA, to classify histopathological pictures of lung and colon tumors from the LC25000 dataset [7]. The authors extracted both first- and second-order features and used combinations of them to find the subset that provides the optimal discriminating ability. As a preprocessing method, the researchers used Unsharp Masking in order to enhance contrast and sharpen the original image to emphasize texture and detail. The accuracy results were satisfactory, and using the XGBoost algorithm, the highest accuracy was obtained (95.6%). Another study highlighted the importance of Whole-Slide Images (WSIs) as digital replicas of stained specimen samples, contrasting them with traditional glass slides [8; 9]. WSIs are integral to the pathology diagnosis process, facilitating easy data sharing and storage [10; 11]. They provide pathologists with comprehensive insights into data content, aiding in the precise diagnosis of tumor and cancer subtypes. In recent years, various approaches have been applied to WSI segmentation and classification, with many focusing on superficial features like texture and pattern recognition [12], gray-level co-occurrence matrixes [13], or local binary patterns [14]. However, these techniques often encounter limitations due to the diverse texture, shape, and color distributions present in each tumor simultaneously [15]. Modern histopathological images are notably large, containing up to billions of pixels, thus presenting high-level complex clinical features. A previous study investigated the use of machine learning to create custom models for the slide-level classification and multi-class segmentation of colon cancer images, resulting in an AUC of 0.71 [16]. This method involves building models from the ground up rather than using pre-trained models, and while computationally costly and experimental, it allows for improving the model's ability to learn and adapt to the distinct features found in different pathology images. Furthermore, the addition of multi-class segmentation enables these models to be used for a wide range of cancer types (e.g., tumor stroma, necrosis, and lymph, which are present in various cancer types), hence increasing therapeutic utility. In this study, we describe an algorithmic approach, highlighting machine learning's transformative promise in computational pathology and customized medicine. A significant gap in the literature exists regarding the optimized use of computational models for the precise classification of colorectal cancer images, despite advancements in image analysis technology. While convolutional neural networks (CNNs) are recognized for their effectiveness in image recognition, the research remains limited in identifying the most suitable architectures and techniques for classifying colorectal cancer into distinct pathological categories. This study addresses this gap by utilizing a comprehensive dataset annotated into eight specific categories of cancerous cell structures. By comparing CNN-based image classification approaches with other methods, this research aims to determine the most effective techniques for accurately distinguishing between these diverse cell categories. Further, many studies often focus solely on raw image data, overlooking the potential impact of feature extraction techniques that could enhance model interpretability and provide richer insights into histopathological characteristics.