Two subtypes of lung cancer classification from histopathology images based on deep learning

Abstract: Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common forms of lung cancer. These two subtypes of lung cancer are usually classified by visual inspection clinically. Our aim is to design an effective strategy based on convolutional neural networks to classify histopathology slides of these two types of lung cancer. With augmentation of the histopathology slides, different classifiers were trained and three ensemble learning methods were compared in this project. Finally, we determined which training strategy that produced the best result. In the case of limited samples, we find that the combination of transfer learning and ensemble learning greatly improves the classification accuracy for whole-slide images of lung tissue. The optimal strategy achieved 94.2% accuracy with 120 training cases and 86.3% accuracy with 80 independent test cases. We consider this comprehensive strategy remarkable in solving the classification problem with the different kinds of lung cancer.