A Hybrid Deep Learning Approach for Lung Nodule Classification

Abstract

Lung cancer has the highest morbidity and mortality rates worldwide. Pulmonary nodules are an early manifestation of lung cancer. Therefore, accurate classification of pulmonary nodules is of great significance for the early diagnosis and treatment of lung cancer. However, the classification of lung nodules is a complex and time-consuming task requiring extensive image reading and analysis by expert radiologists. Therefore, using deep learning technology to assist doctors in detecting and classifying pulmonary nodules has become a current research trend. A lightweight classification model named Res-VGG is proposed for classifying lung nodules as benign or malignant. The Res-VGG model improves on VGG16 by reducing the use of convolutional and fully connected layers. To reduce overfitting, residual connections are introduced. The training of the model was performed on the LUNA16 database, and a ten-fold cross-validation method was used to evaluate the performance of the model. In addition, the Res-VGG model was compared with three other common classification networks, and the results showed that the Res-VGG model outperformed the other models in terms of accuracy, sensitivity, and specificity.