DeepLUAD: An efficient approach for lung adenocarcinoma pattern classification

Abstract

Histopathological analysis of whole-slide images is the gold standard technique for diagnosis of lung cancer and classifying it into types and subtypes by specialized pathologists. This labor-based approach is time and effort consuming, which led to development of automatic approaches to assist in reducing the time and effort. Deep learning is a supervised classification approach that is well adapted for automatic classification of histopathological images. We aimed to develop a deep learning-based approach for lung adenocarcinoma pattern classification and generalize the proposed approach to the classification of the major non-small cell lung cancer types. Three publicly available datasets were used in this study. A deep learning approach for histopathological image analysis using convolutional neural networks was developed and incorporated into automatic pipelines to accurately classify the predominant patterns on the whole-slide images level and non-small cell lung cancer types on patch-level. The models were evaluated using the confusion matrix to perform an error analysis and the classification report to compute F1-score, recall and precision. As results, the three models have shown an excellent performance with best combination of hyper-parameters for training models. First and second models predicted adenocarcinoma predominant patterns on two different datasets with an accuracy, respectively, of 96.15% and 89.51%. The third model has exceeded an accuracy of 99.72% in classifying major non-small cell lung cancer types. The proposed deep learning-based lung cancer classification approach can be used to assist pathologists in identifying of lung adenocarcinomas patterns.