Classification of Histopathological Images of Colon Cancer Using Convolutional Neural Network Method

Colon cancer is cancer with the most deaths in Indonesian society. Detection of disease through histopathological images of colon cancer still uses manual methods with readings by doctors. So it is necessary to do a system to detect and classify colon cancer. This study aims to create a colon cancer classification system to reduce the time in classifying the categories of colon cancer. In this study, a classification system for colon cancer was created into two classes, namely adenocarcinomas and polyps. Colon cancer data used in this study is data obtained online through the Kaggle website which consists of 2000 histopathological images measuring 768 pixels in jpeg format. The system is built using the Convolutional Neural Network (CNN) method with the MobileNet architecture. The design of this system is made by analyzing parameters that affect system performance based on the influence of image size, optimizer, learning rate, activation function, and batch size. Parameters used in evaluating system performance are accuracy, precision, recall, and f1-score. The results of testing the system based on parameters obtained the best model with image size 224x224 pixels, Adam optimizer, learning rate 0.0001, sigmoid activation function, and batch size 40. The best results of the best model are 100% accuracy value, 100% precision value, 100% recall value, and 100% f1-score with a loss of 0.000135.