Publication

Abstract : In this study, we propose a cost-effective computer-aided detection system based on machine learning for the classification of colorectal cancer tissues. Colorectal cancer stands as the third most prevalent cancer globally and is the second leading cause of malignancy-related deaths. The proposed computer-aided detection system involves partitioning each image out of 7180 histopathological images into 16 equal-sized blocks. Subsequently, features are extracted from each block of image in RGB (red, green, blue), HSV (hue, saturation, value), and L*a*b* color spaces. The extracted features include regional and gray-level co-occurrence matrix features. Following the extraction, these features undergo scaling to eliminate outliers before being input into a machine learning classifier. The performance of the machine learning models is enhanced by optimizing the hyperparameters of the models. Notably, CatBoost outperformed all other models, achieving an exceptional accuracy of 95.19%. This remarkable accuracy indicates CatBoost as a promising model for the task of colorectal cancer tissue classification.