A Hybrid Model: DGnet-SVM for the Classification of Pulmonary Nodules


We investigate the problem of benign and malignant pulmonary nodules classification for thoracic Computed Tomography (CT) images. Although various methods have been proposed to solve this problem, they have bottlenecks of poor input image quality and subjective or shallow feature extraction. In this paper, we propose a Denoise GoogLeNet model with the classifier of Support Vector Machine (DGnet-SVM) to improve the final classification accuracy. We apply Denoise Network to improve the CT image quality by reducing the noise, and GoogLeNet is utilized to extract high-level features for better generalization of data. Furthermore, SVM is applied to classify the nodules owing to its great classification performance. The experimental results show that our hybrid model outperforms other state-of-the-art methods with the accuracy of 0.89 based on five-fold cross validation and the AUC is 0.95. The advantages of the proposed model and our future work are also discussed.

Neural Information Processing