Deep Learning for Automatic Identification of Nodule Morphology Features and Prediction of Lung Cancer

Abstract

Lung Cancer is the most common and deadly cancer in the world. Correct prognosis affects the survival rate of patient. The most important symptom for early diagnosis is nodules images in CT scan. Diagnosis performed in hospital is divided into 2 steps : (1) Firstly, detect nodules from CT scan. (2) Secondly, evaluate the morphological features of nodules and give the diagnostic results.In this work, we proposed an automatic lung cancer prognosis system. The system has 3 steps : (1) In the first step, we trained two models, one based on convolutional neural network (CNN), and the other recurrent neural network (RNN), to detect nodules in CT scan. (2) In the second step, convolutional neural networks (CNN) are trained to evaluate the value of nine morphological features of nodules. (3) In the final step, logistic regression between values of features and cancer probability is trained using XGBoost model. In addition, we give an analysis of which features are important for cancer prediction. Overall, we achieved 82.39% accuracy for lung cancer prediction. By logistic regression analysis, we find that features of diameter, spiculation and lobulation are useful for reducing false positive.