EMCA-UNet: A Novel Multi-Scale Convolutional Attention Model for Enhanced Lung Nodule Segmentation

Abstract

Pulmonary nodule segmentation plays a crucial role in the early detection of lung cancer. However, existing detection methods fail to efficiently extract multi-scale features and precisely reconstruct nodule boundaries, especially when dealing with nodules of varying sizes, irregular shapes, and complex backgrounds. To address this challenge, we introduce a novel U-Net architecture called EMCA-UNet (Efficient Multi-scale Convolution Attention). Specifically, the encoder of EMCA-UNet comprises Residual Multi-scale Attention Convolution (RESMAC) blocks, which enhance the model's feature extraction capabilities. The decoder integrates three modules: Multi-scale Convolution Attention (EMCA), Large Kernel Attention Gates (LGAG), and Efficient Up-Convolution Blocks (EUCB). These modules synergistically form a new multi-scale attention decoding layer that replaces the traditional U-Net decoder structure, thus enabling efficient multi-scale feature fusion and precise boundary reconstruction. Research has demonstrated that EMCA-UNet outperforms traditional models on publicly available datasets, such as LIDC-IDRI and LNDb. The Dice coefficient improved from 0.9198 to 0.9304, and the IoU increased from 0.8727 to 0.8856. Experimental results demonstrate that the proposed method offers a novel perspective for pulmonary nodule segmentation.