Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images

Abstract

Lung cancer is one of the four major cancers in the world. Accurate diagnosing of lung cancer in the early stage plays an important role to increase the survival rate. Computed Tomography (CT)is an effective method to help the doctor to detect the lung cancer. In this paper, we developed a multi-level convolutional neural network (ML-CNN)to investigate the problem of lung nodule malignancy classification. ML-CNN consists of three CNNs for extracting multi-scale features in lung nodule CT images. Furthermore, we flatten the output of the last pooling layer into a one-dimensional vector for every level and then concatenate them. This strategy can help to improve the performance of our model. The ML-CNN is applied to ternary classification of lung nodules (benign, indeterminate and malignant lung nodules). The experimental results show that our ML-CNN achieves 84.81\% accuracy without any additional hand-craft preprocessing algorithm. It is also indicated that our model achieves the best result in ternary classification.

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