Benign-malignant classification of pulmonary nodule with deep feature optimization framework

Abstract

Convolutional neural network (CNN) has been widely utilized for benign-malignant classification of pulmonary nodules in Computed Tomography images. For traditional CNN models, single-input strategy limits the ability of feature extraction, while multi-input CNN models usually achieve better performance by exploring comprehensive information with pulmonary nodules. However, the concatenation layer in multi-input CNN methods generates high-dimensional deep features, which can easily bring about the curse of dimensionality. To tackle these issues, a manifold-based deep learning model termed deep feature optimization framework (DFOF) is proposed to perk up the performance. In feature extraction stage, a two-stream network is adopted for extracting perinodular and intranodular features from CT images, which forms high-dimensional features. In feature optimization stage, a manifold optimization process is proposed to compact intraclass neighbors while separating interclass samples in low-dimensional embedding space. After that, the optimization features are classified by classifiers, such as nearest neighbor, support vector machine, and random forest. Experiments were conducted using two datasets with 5-cross-validation. The accuracy, area under curve, precision, recall, and F-score reach 92.13%, 95.54%, 94.16%, 87.16%, and 89.93% on the LIDC-IDRI dataset, 90.03%, 94.06%, 96.95%, 89.91%, and 93.38% on the external validation dataset. The results indicate that DFOF has a remarkably better benign-malignant classification performance than several state-of-the-art methods.