Lung cancer is the leading cause of cancer-related mortality, of which non-small cell lung cancer is the most common type, that furthermore is divided into four subtypes. The primary tumor of these subtypes can be divided into four stages of cancer. This is performed by pathologists on histopathological images. However, due to the number of cells in a sample and the staining intensities which can vary, inter-observer variability is likely to occur when diagnosing tumor subtypes and stages.
Existing methods have classified lung cancer into subtypes using CNNs, but it has not yet been investigated whether a CNN could be used to determine the stage of lung cancer, which will be investigated in this study. Data from The Cancer Genome Atlas has been acquired from the bronchus and lung subset. Data selection and preprocessing were performed to prepare the data and ensure homogeneity in the datasets, and the AlexNet was fine-tuned using a pretrained model. Four different experiments were conducted due to a concern about noisy labels. Furthermore, three WSI-classification approaches to evaluate the predicted labels were performed in each of the experiments.
The trained model was tested patch-based and WSI-based. The patches were classified with an accuracy of 0.52+/-0.04 during the four experiments. The experiments with the best results had an accuracy of 0.56 in all three approaches. Additionally, two of the approaches had the highest accuracies of 0.56+/-0.01 when classifying WSI-based.