The aim of this project is to correctly classify 25,000 histopathology images of lung and colon cancer from the following dataset: https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images. Each image belongs to one of five different classes (three for lung tumors and two for colon tumors). To achieve this, a convolutional neural network is created, which through convolutional layers and optimization layers, accurately classifies these images. Before training this model, we must appropriately process the images to ensure they are correctly divided into the proper sets (training, validation). In this problem, the technique of data augmentation was not used because the dataset documentation indicates that the images are already augmented.