Cross Fusion‐ CapNets : Interpretable Modeling for Benign‐Malignant Classification of Lung Nodules

ABSTRACT

Early and reliable classification of benign and malignant pulmonary nodules remains challenging because nodule appearances are highly heterogeneous and most deep learning models provide limited diagnostic transparency. To address these issues, we propose Cross Fusion‐CapNets, an interpretable model that combines a Multi‐scale Fusion Network (MSFNet) with a capsule‐based explanation module. MSFNet integrates wavelet‐based local features and attention‐based global features through a Multi‐feature Fusion (MFF) Block, while the capsule module learns attribute‐oriented representations associated with eight radiological characteristics from LIDC‐IDRI. Experiments on LIDC‐IDRI achieved 95.4% accuracy and 99.2% AUC for benign‐malignant nodule classification. As a cross‐modality image‐level assessment on the LC25000 dataset, five‐fold stratified cross‐validation and independent partition evaluation yielded 99.96% ± 0.04% validation accuracy and 99.97% ± 0.03% evaluation accuracy. These results show that the proposed model combines accurate classification with attribute‐level interpretability.