Attention-enhanced 3D convolutional neural network for automated lung nodule malignancy assessment

Abstract

Accurate classification of lung nodule malignancy remains a critical challenge in medical imaging, directly impacting early lung cancer diagnosis and patient outcomes. While existing 3D convolutional neural networks have shown promise in capturing volumetric morphological characteristics, effectively focusing on the most discriminative features for malignancy classification remains challenging. This study investigates a customized 3D ResNet architecture enhanced with Convolutional Block Attention Module (CBAM) for improved lung nodule malignancy classification, with validation across multiple datasets to demonstrate generalizability. Our proposed framework integrates CBAM attention mechanisms into a 3D ResNet backbone, enabling the model to adaptively focus on both spatial and channel-wise features critical for malignancy determination. The CBAM module allows the network to emphasize informative features while suppressing irrelevant noise commonly present in CT imaging. This attention-guided approach addresses the inherent challenge of extracting discriminative features from volumetric medical data where subtle morphological differences often determine malignancy status. The 3DResNetarchitecture uses residual connections to facilitate deep feature learning while reducing gradient vanishing problems, particularly important when processing high-dimensional volumetric data. By operating directly on 3D patches, our model captures spatial relationships across all three dimensions, providing a more comprehensive representation of nodule characteristics including shape irregularities, internal texture patterns, and boundary definitions that are crucial malignancy indicators. To enhance model interpretability and validate attention mechanisms, we generate spatial attention maps that visualize regions of focus during classification decisions. These attention maps demonstrate that our CBAM-enhanced model successfully identifies clinically relevant anatomical structures and morphological features associated with malignant nodules. Additionally, we conducted texture feature experiments to complement the deep learning approach. Traditional texture analysis methods were employed to extracthandcrafted features such as gray-level co-occurrence matrix (GLCM) statistics, as well as classification of nodules based on texture. These experiments provide additional insights into nodule characteristics, and when combined with the spatial attention maps generated by our model, offer a complementary view of how both traditional handcrafted features and learned attention mechanisms contribute to understanding nodule texture patterns and properties. Experimental validation was performed across multiple lung nodule datasets, both public and private, demonstrating the generalizability of our approach. The crossdataset evaluation confirms that the architecture maintains robust performance across different imaging protocols, patient populations etc., as well as consistence in all metrics across all tested datasets. The spatial attention maps revealed that the model correctly learns to focus on the relevant regions, aligning with radiological expertise and improving clinical interpretability. This work contributes to the advancement of computer-aided diagnosis systems for lung cancer screening by demonstrating the effectiveness of attention-enhanced 3D CNNs. The proposed CBAM-integrated 3D ResNet architecture offers a promising solution for automated lung nodule malignancy classification, potentially supporting radiologists in making more accurate and timely diagnostic decisions.