Classification of Lung Nodule CT Images Using GAN Variants and CNN

Abstract

Global Cancer Statistics 2020 states that there are 2.2 million lung cancer cases worldwide with 1.8 million deaths. At present, deep learning based CAD system for lung nodules classification has been extensively explored. However, this approach requires a great size of images which becomes an issue for medical images. Thus, Generative Advesarial Network (GAN) is introduced to ease this limitation by creating synthetic images. In this study, four GAN architectures namely Deep Convolutional (DCGAN), Deep Regret Analytic GAN (DRAGAN), Wasserstein GAN (WGAN) and Wasserstein GAN with Gradient Penalty (WGANGP) are used in generating synthetic medical images which are then used to classify the lung lesions into benign and malignant via ShuffleNet. The classification is assessed based on pecificity, accuracy, sensitivity, and values of AUC-ROC. Experimental results show that DRAGAN achieved the lowest Fréchet Inception Distance (FID) score of 137.48 of the new generated datasets followed by the WGAN-GP (158.86), WGAN (176.86) and DCGAN (172.56). However, due to the lack of diversity in datasets of DRAGAN, instead WGAN-GP ShuffleNet performed the best in the classification task achieving 98.87% of accuracy, 98.36% of specificity, 99.34% of sensitivity and highest AUC among others at 99.96%. Overall, both high quality and well diversed synthetic images are equally important for the lung nodules classification problem.