Lung Nodule Classification Strategy via Dual-Branch Capsule Network With Routing Optimization

Abstract

Lung cancer is one of the cancers with the highest incidence and mortality rates worldwide. The large volume of CT images and the limited resources of radiologists have highlighted the demand for computer-aided diagnostic (CAD) systems. Among existing methods, interpretable capsule networks face prominent challenges in pulmonary nodule classification, such as insufficient feature extraction and high computational costs associated with routing algorithms. To address these issues, this study proposes the BiCaps-VG model, which comprises three components: dual-branch feature extraction, capsule vector routing, and multi-task learning. One branch incorporates a receptive field adaptation module that employs multi-receptive field parallel heterogeneous convolutional paths to extract scale-sensitive nodule features. The other branch is a hierarchical semantic recovery module, which first adopts an encoder-decoder architecture and then leverages a spatial attention mechanism to model global contextual semantics. These two branches are fused to form a unified semantic representation space, thereby enhancing the representational capacity of nodule features. The capsule routing module adopts a variance-guided capsule routing algorithm that calculates activation values based on the variance between predicted low-level capsules and high-level capsules, thus avoiding iterative routing and reducing computational overhead. In the multi-task module, the extracted nodule attribute capsules are used for malignancy prediction, attribute grade classification, and mask reconstruction. Experimental results demonstrate that BiCaps-VG achieves an accuracy of 94.88% in pulmonary nodule malignancy grading, representing a 1.58% improvement over the baseline model. Combined with its inherent interpretability, the model exhibits considerable potential for clinical application. The code is available at https://github.com/fzhou924anpan-prog/BiCaps-VG.