Automated Lung and Colon Cancer Classification using Histopathological Images

Abstract Background Cancer is the leading cause of mortality in the world. And among all cancers lung and colon cancers are two 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. Methods 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. 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. Results The Swin Transform V2 model achieved perfect results on all metrics, and it outperformed other models of this study and all models of previous studies. Conclusions The Swin Transformer V2 model has the potential to be used to assist pathologists in classifying lung and colon cancers using histopathology images. Moreover, the LC25000 dataset is too easy and should no longer be used independently.