Purpose: Lung cancer usually presents as pulmonary nodules on early diagnostic images, and accurately estimating the malignancy of pulmonary nodules is crucial to the prevention and diagnosis of lung cancer. Recently, deep learning algorithms based on convolutional neural networks have shown potential for pulmonary nodules classification. However, the size of the nodules is very diverse, ranging from 3 to 30 mm, which makes classifying them to be a challenging task. In this study, we propose a novel architecture called Res-trans networks to classify nodules in computed tomography (CT) scans.
Methods: We designed local and global blocks to extract features that capture the long-range dependencies between pixels to adapt to the correct classification of lung nodules of different sizes. Specifically, we designed residual blocks with convolutional operations to extract local features and transformer blocks with self-attention to capture global features. Moreover, the Res-trans network has a sequence fusion block that aggregates and extracts the sequence feature information output by the transformer block that improves classification accuracy.
Results: Our proposed method is extensively evaluated on the public LIDC-IDRI dataset, which contains 1,018 CT scans. A tenfold cross-validation result shows that our method obtains better performance with AUC = 0.9628 and Accuracy = 0.9292 compared with recently leading methods.
Conclusion: In this paper, a network that can capture local and global features is proposed to classify nodules in chest CT. Experimental results show that our proposed method has better classification performance and can help radiologists to accurately analyze lung nodules.
Keywords: Computer-aided diagnosis; Deep learning; Lung nodules classification; Transformer.
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