Background: Developing deep learning networks to classify between benign and malignant lung nodules usually requires many samples. Due to the precious nature of medical samples, it is difficult to obtain many samples.
Objective: To investigate and test a DCA-Xception network combined with a new data enhancement method to improve performance of lung nodule classification.
Methods: First, the Wasserstein Generative Adversarial Network (WGAN) with conditions and five data enhancement methods such as flipping, rotating, and adding Gaussian noise are used to extend the samples to solve the problems of unbalanced sample classification and the insufficient samples. Then, a DCA-Xception network is designed to classify lung nodules. Using this network, information around the target is obtained by introducing an adaptive dual-channel feature extraction module, and the network learns features more accurately by introducing a convolutional attention module. The network is trained and validated using 274 lung nodules (154 benign and 120 malignant) and tested using 52 lung nodules (23 benign and 29 malignant).
Results: The experiments show that the network has an accuracy of 83.46% and an AUC of 0.929. The features extracted using this network achieve an accuracy of 85.24% on the K-nearest neighbor and random forest classifiers.
Conclusion: This study demonstrates that the DCA-Xception network yields higher performance in classification of lung nodules than the performance using the classical classification networks as well as pre-trained networks.
Keywords: Lung nodule classification; Wasserstein Generative Adversarial Networks (WGAN); Xception; classifier; convolutional attention module.