Classification of Lung and Colon Cancer Histopathological Images Using Convolutional Neural Network (CNN) Method on a Pre-Trained Models

Abstract

Cancer is a severe illness that can affect many young and older people. In Indonesia, lung cancer is the leading cause of cancer-related death, whereas colon cancer, with more than 1.8 million cases worldwide in 2018, is the third most common cancer. This study intends to create a model to categorize histological images of lung and colon cancer into five labels to aid medical professionals' categorization job. This study uses a pre-trained model idea known as VGG19 in its CNN (Convolutional Neural Network) technique. The dataset uses 25,000 histological graphic pictures with a ratio of 80% training data and 20% testing data. The classification system for lung and colon cancer contains five categories: lung benign tissue, lung adenocarcinoma, lung squamous cell carcinoma, colon adenocarcinoma, and colon benign tissue. The training result revealed a 99.96% accuracy rate and a 1.5% loss rate. The model can be rated as excellent based on these results.

Keywords: Lung Cancer, Colon Cancer, Convolutional Neural Network, CNN, Pre-Trained