Automated Lung and Colon Cancer Classification Using Histopathological Images

Abstract

Cancer is the leading cause of mortality in the world. And among all cancers lung and colon cancers are 2 of the most common causes of death and morbidity. The aim of this study was to develop an automated lung and colon cancer classification system using histopathological images. An automated lung and colon classification system was developed using histopathological images from the LC25000 dataset. The algorithm development included data splitting, deep neural network model selection, on the fly image augmentation, training and validation. The core of the algorithm was a Swin Transform V2 model, and 5-fold cross validation was used to evaluate model performance. The model performance was evaluated using Accuracy, Kappa, confusion matrix, precision, recall, and F1. Extensive experiments were conducted to compare the performances of different neural networks including both mainstream convolutional neural networks and vision transformers. The Swin Transform V2 model achieved a 1 (100%) on all metrics, which is the first single model to obtain perfect results on this dataset. The Swin Transformer V2 model has the potential to be used to assist pathologists in classifying lung and colon cancers using histopathology images.

Keywords: Lung cancer classification; Swin Transformer V2; colon cancer classification; histopathological images; vision transformer.

Figure 1.

The flowchart of automated lung and colon cancer classification system. The image of Swin Transformer V2 was adopted from https://github.com/microsoft/Swin-Transformer.

Figure 2.

Five-fold cross validation data splitting. Blue, cyan and green stand for training, validation and testing dataset, respectively. The final testing dataset was combined by 5 testing subsets.

Figure 3.

Representative image patches of LC25000 dataset.