# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 1 Introduction  
Cancer remains one of the foremost challenges in modern medicine, with its complex and heterogeneous nature necessitating advanced diagnostic tools and methodologies. Histopathological analysis, a cornerstone in cancer diagnosis, provides valuable insights into the morphological characteristics of tissue samples. Recent strides in technology have ushered in an era of computational pathology, leveraging machine learning and computer vision techniques to enhance the accuracy and efficiency of cancer diagnostics. This study delves into the realm of computational models applied to histopathological images, with a specific focus on colon cancer. Colorectal cancer (CRC), a prevalent and potentially fatal malignancy, demands rigorous examination and precise classification for effective diagnosis and treatment planning. Traditional methods of histopathological analysis, while invaluable, are often labor-intensive and subject to interobserver variability. The advent of computational models, particularly machine learning algorithms, present a promising avenue for automating and augmenting the diagnostic process. Yet, in the realm of classifying tissue types in CRC histological images, numerous published methods solely account for two categories of tissue, namely tumor and stroma. This limitation renders these approaches inadequate for addressing the more heterogeneouscomponents of tumors [1; 2]. [PERSON] and his colleagues introduced a novel dataset (Kather-CRC-2016) comprising 5000 histological images of human colorectal cancer, encompassing eight distinct tissue types [3]. Leveraging this dataset, they evaluated the classification efficacy of various texture descriptors and classifiers across the eight tissue types. Their findings revealed an optimal classification approach that surpassed conventional methods, enhancing the state of the art for tumor-stroma differentiation with an accuracy increase from 96.9% to 98.6% for specific tissue types. Moreover, their method set a new benchmark for multiclass tissue differentiation, achieving an accuracy of 87.4% across the eight different classes. One previous attempt employed a deep neural network methodology to effectively differentiate between various tumor types utilizing histology colorectal images sourced from 28 ordinary plus 29 cancerous tissue samples acquired from the National Cancer Center, which is located in South Korea [4]. The dataset ultimately comprised 6806 images of normal tissues and 3474 images of cancerous tissues. The researchers devised five adaptations of the Visual Geometry Group (VGG) system and subjected them to two distinct tests. For the first test, they identified the configuration of VGG for the dataset, achieving 94.30% maximum accuracy. Subsequently, they applied the strongest altered VGG configuration to assess the CNN model's performance. By utilizing the dataset with the modified VGG-E configuration, they attained 93.48% accuracy, translating to the correct classification of 294 healthy tissue images out of 309 and 667 cancerous tissue images out of 719. Furthermore, the so-called \"light weighted CNN\" was proposed as a new architecture for the convolutional neural network. It utilizes different combinations of convolutional layers, activation layers, and other parameters [5]. The proposed CNN used a dataset including eight different types of tissue, and the experimental results showed that the misclassification rate was 22.7%, which was deemed superior to other simple architectures and classifiers. Two other deep learning approaches were proposed to recognize CRC tissue types using the Kather-CRC-2016 dataset [6]. In the first method, the authors combined the predictions of different pre-trained neural network classifiers (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161) and obtained the mean posterior prediction probability for each tissue. In the second method, a combination of deep features obtained from the last fully connected layers of the CNN is performed in order to feed the NN's four layers with those features.