3D-DHA-MAFNet: A Multi-View Attention-Enhanced Deep Learning System for Lung Nodule Detection and Classification

Abstract

Lung cancer, one of the most lethal malignancies globally, has a high mortality rate, making early screening and accurate diagnosis crucial for improving patient survival rates. Low-dose computed tomography (LDCT) has significantly increased the detection rate of lung nodules, but the vast amount of imaging data poses a significant challenge for manual interpretation. This article proposed a lung nodule computer-aided quantitative diagnostic system based on a three-dimensional convolutional neural network (3D CNN), named 3D-DHA-MAFNet. By integrating deformable convolution network (DCN), hybrid attention mechanism (HAM), and multi-view adaptive fusion network (MAFN), this system significantly enhanced the detection and benign/malignant classification capabilities for small and irregular lung nodules. The model was trained and evaluated on the lung nodule analysis 2016 (LUNA16) and LIDC-IDRI datasets with a patient-level split in the ratio of 7:2:1. The number of training epochs was 200. Data augmentation methods included random rotation (±15°), translation (±5 pixels), and elastic deformation. The performance was compared with baseline models including 3D-UNet, AttentNet, 3DAGNet, 3D multi-scale capsule network (3D-MCN), and 3D multi-view SE CNN (3D MVSECNN). Experiments showed that on the LUNA16 dataset, the model achieved a detection sensitivity of 98.7% with a low false-positive rate of 1.2 per scan, which was a notable improvement over baseline models like 3D-UNet. On the LIDC-IDRI dataset, the classification accuracy for benign and malignant nodules reached 96.8%, with an AUC value of 0.983, and particularly, the sensitivity for malignant nodules was enhanced to 97.5%. Ablation studies verified the synergistic gains of each module, and cross-clinical dataset testing further demonstrated its strong generalization ability. This article presents a high-precision and interpretable intelligent diagnostic tool designed for clinical applications, which holds strong potential to promote the clinical translation of early lung cancer screening.