Feature Reconstruction‐Guided Multi‐Scale Attention Network for Non‐Significant Lung Nodule Detection

ABSTRACT Lung cancer remains the leading cause of cancer‐related incidence and mortality worldwide. Early detection of lung nodules is crucial for significantly reducing the risk of lung cancer. However, due to the high similarity in CT image features between lung nodules and surrounding normal tissues, nodules are often missed or misidentified during the detection process. Moreover, the diverse types and morphologies of nodules further complicate the development of a unified detection approach. To address these challenges, this study proposes a novel Feature Reconstruction‐guided Multi‐Scale Attention Network (FRMANet). Specifically, a Refined Feature Reconstruction Module is designed to effectively suppress redundant information while preserving essential feature representations of nodules, ensuring high sensitivity and enhanced representation capability for nodule regions during feature extraction. Additionally, a Multi‐scale Feature Enhancement Attention mechanism is introduced, which utilizes an attention‐based fusion strategy across multiple scales to fully capture discriminative features of nodules with varying sizes and shapes. Experimental results on the LUNA16 dataset demonstrate that the proposed FRMANet achieves superior detection performance, with a mAP of 0.894 and an F1 score of 0.923, outperforming existing state‐of‐the‐art methods.