Deep Learning for Lung Cancer Treatments

Using the data on lung cancer from Surveillance, Epidemiology, and End Results (SEER) cancer registry, a team of researchers at PennState assessed the survival period predictions by three DL architectures – Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) – as well as compared the DL models’ performance against traditional machine learning models. Both cancer survival classification and regression approaches were implemented.

The findings show that under certain conditions DL models’ accuracy was 71.18% while the traditional machine learning models predicted the survival periods with an accuracy rate of 61.12%. 

The researchers also conducted feature importance analysis to investigate the model interpretability, i.e. to evaluate how a combination of relevant factors, such as types of cancer, size of tumours, or the speed of tumour growth, impact lung cancer survival periods.