# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 4 Discussion  
Cancer persists as a leading hurdle in contemporary medicine, requiring sophisticated diagnostic tools and methodologies due to its intricate and diverse characteristics. Histopathological examination, essential in cancer diagnosis, offers a significant understanding of the structural features of tissue specimens. The central aim of this research is to utilize computational models for the classification of colon cancer images, in order to discern the most effective approach for distinguishing between diverse categories of colon cancer cells. For the image classification task, an extensive dataset, annotated into eight distinct categories representing various aspects of cancerous cell portions, was used. More specifically, the dataset contained 5000 non-overlapping 150 \(\times\) 150 tissue images of the following types: tumor epithelium, complex stroma, immune cells, simple stroma, debris, adipose tissue, empty tissue, and normal mucosal gland. For each picture, 14 features were extracted and used for the classification task. The features consisted of first-order statistical measures, such as variance and mean pixel intensity, but we also included more sophisticated features, such as local binary patterns and Gabor filters. For the image classification task, we utilized seven algorithms: the convolutional neural network, the k-NN algorithm, the Support Vector Machine, the random forest, the naive Bayes, the conditional inference tree, and the XGBoost algorithm. Each algorithm was trained on a training subset of the data, and the optimal parameters for each algorithm were determined by using cross-validation on the dataset. In order to assess the adequacy of the algorithms, five metrics (accuracy, sensitivity, specificity, precision, and F1 score) were utilized and they were used on the test set, comparing the various methods. The XGBoost algorithm achieved the highest accuracy, correctly classifying tissue images, and its estimated accuracy was nearly 90% on the test set; yet all algorithms, apart from the k-NN algorithm, achieved satisfactory results. More specifically, the convolutional neural network, the random forest algorithm, and the SVM achieved high accuracy on the test set as well, with a small deviation from the leading algorithm. Regarding sensitivity, specificity, precision, and the F1 score, the four algorithms had nearly identical results, indicating that they could be used as a screening tool for tissue image classification. Regarding tissue types, not all types were equally supported by the algorithms. Not surprisingly, empty images had the highest scores regarding the evaluation metrics, as it is evident that the algorithms could easily \"see\" that they contain minor pieces of tissue. Furthermore, adipose tissue and normal mucosal glands images achieved high classification rates. Adipose tissue images mainly consist of red and white colors, and they are similar to empty images,while mucosal glands contain specific patterns. On the other hand, complex stroma had the lowest classification rate, as they had different color and shape patterns. Our main findings align with studies in which different algorithm architectures have been used to classify colon cancer tissue images. One of the first attempts to achieve this with the same sample images utilized a variety of image classifiers and achieved an overall 87.4% accuracy for the eight tissue types, which is slightly lower than our proposed test accuracy. One other attempt, which was more computationally intense, used a sequential method with two different neural network architectures, and the authors achieved an accuracy of 96% on the same dataset [6]. Other, more feasible methods have been used on the sample. For example, in a previous study, an accuracy of 77.3% was achieved on the same dataset using a CNN with smaller complexity. Furthermore, a previous study using a deep neural network approach reported that the highest obtained accuracy was 93.48% on 29 tumor samples.