Multi-resolution cnn and knowledge transfer for candidate classification in lung nodule detection

The automatic lung nodule detection system can facilitate the early screening of lung cancer and timely medical interventions. However, there still exist multiple nodule candidates produced by initial rough detection in this system, and how to determine authenticity is a key problem. As this work is often challenged by the radiological heterogeneity of the computed tomography scans and the variable sizes of lung nodules, we put forward a multi-resolution convolutional neural network (CNN) to extract features of various levels and resolutions from different depth layers in the network for classification of lung nodule candidates. Through the use of knowledge transfer, the method can be divided into three steps. First, we transfer knowledge from the source CNN model which has been applied to edge detection and improve the model to a new multi-resolution model which is suitable for the image classification task. Then, the knowledge is transformed from source training progress so that all of the side-output branches in the model will be considered in the calculation. Moreover, the loss function and objective equation are improved to be image-wise calculation rather than pixel-wise. Finally, samples production and data enhancement are performed to train and test a classifier tailored for classification of lung nodule candidates. The experimental results on the LUNA16 data set show that our method gets an accuracy of 0.9733, a precision of 0.9673, and an AUC of 0.9954 while being used for lung nodule candidate classification, which is higher than the scores obtained by most of the state-of-the-art approach. In addition, when the test samples with three different sizes of 26∗26, 36∗36, and 48∗48 are used to test the multi-resolution CNN, the accuracy rate of all three experiments exceed 92.81%, which demonstrates that the proposed model is insensitive to input scales.