Enhanced Pulmonary Nodule Detection via Two‐Stage 3D CNN With Multiscale Attention and Kolmogorov–Arnold Activation

Timely and precise identification of pulmonary nodules via CT imaging plays a pivotal role in effective lung cancer screening and diagnosis. However, traditional nodule detection largely relies on radiologists’ visual inspection, which is not only labor‐intensive but also subjective. This paper introduces a novel two‐stage three‐dimensional convolutional neural network for automated pulmonary nodule detection. In the first stage, an adaptive multiscale dual attention (AMDA) mechanism is introduced to enhance feature representation across varying spatial scales. In the second stage, a Kolmogorov–Arnold layer (KAL) is integrated to strengthen nonlinear feature modeling and improve discrimination between true nodules and hard negative samples. Based on extensive experiments conducted on the LUNA16 dataset, the results indicate that the proposed framework achieves competitive performance across a wide range of false‐positive rates. Furthermore, cross‐dataset evaluation on the LIDC‐IDRI dataset validates the generalization capability of the proposed method. Our approach not only advances automated nodule detection but also provides valuable support for radiologists in clinical settings.