Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT

Abstract

Lung cancer is caused by abnormal and uncontrolled growth of cells in the lungs and the mortality rate of lung cancer is the highest among all types of cancer. It can be identified and treated with the help of computed tomography (CT) images. For an automated classifier, identifying good features from an image is a key concern. Deep feature extraction using pre-trained convolutional neural networks has been successful for some image domains recently. In our study, we apply a pre-trained convolutional neural network (CNN) to extract deep features from lung cancer CT images and then train classifiers to predict short and long term survivors. The best accuracy of 77.5% was with a cropping approach using a decision tree classifier in a leave one out cross validation with ten features chosen using symmetric uncertainty feature ranking. We mixed extracted deep neural network features along with quantitative (traditional image) features and obtained the best accuracy of 82.5% with a nearest neighbor classifier in a leave one out cross validation using the symmetric uncertainty feature ranking algorithm.