Colorectal Carcinogenesis Identification Using Microphotographs of Rat Liver

This work investigates the use of machine learning models for identifying Colorectal Carcinogenesis (CRC) based on changes in liver tissue, as represented in microphotographs. It was provided by the Enteric Neural Plasticity Laboratory of the State University of Maringá, which is involved in this investigation, analyzing the impact of CRC on other tissues such as the liver. There is a high demand for image analysis, and research for identifying CRC using rat liver microphotographs is limited to visual inspection, which is subjective when made by humans. Therefore, automation could expedite this process by utilizing machine learning models. For this purpose, the Deep Learning models ResNet50, InceptionV3, DenseNetB0, and EfficientNet121 were evaluated as feature extractors, along with the classifiers K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and two Multilayer Perceptrons (MLP) for the classification process. The study has demonstrated a significant accuracy of 92.5% in the goal task.