A novel pulmonary nodule classification framework based on mobile edge computing

Classification of benign and malignant pulmonary nodules is a critical task for developing a Computer-Aided Diagnosis (CAD) system of lung cancer. However, the intelligent diagnosis technology is often limited by equipment and space. Therefore, a computer-aided diagnosis model is proposed running in the Mobile Edge Computing (MEC) environment. The novel lung nodule classification framework improved the Deep Convolutional Generative Adversarial Nets (DCGAN). Firstly, CT images after pre-processing are input into GAN to generate new images with similar features. Then, in the training stage, the derivative model of GAN is introduced into the classification of pulmonary nodules. The optimised function means the improved DCGAN has better anti-noise ability and achieves more accurate classification performance. The experimental results showed an accuracy of 88.88%. Compared with the existing methods, the proposed method is superior to other ones in terms of accuracy, sensitivity, specificity, and area under the ROC curve.