我国肝癌的发病率和死亡率都不容乐观，早诊断、早治疗成为改变这一现状的迫切手段．因此，本文提出一种融合注意力机制和空洞卷积的3D ELD_MobileNetV2肝结节分类模型，用于腹部动态增强磁共振图像四分类．首先，将二维网络结构扩展为三维，避免对磁共振图像进行特征提取时出现空间特征损失的现象；其次，基于局部跨通道交互策略，设计了融合局部特征和全局特征的高效通道注意力机制，将其嵌入到MobileNetV2的瓶颈结构中，增强网络对关键特征的提取能力；然后，在深度卷积中引入三维空洞结构，提高卷积核的感受野；同时，使用Leaky ReLU6激活函数替换原始激活函数，提升模型鲁棒性．该模型在120名患者中（肝炎、硬化结节、异型增生、肝细胞癌各30例）进行了测试和验证．实验结果表明，所提出的模型相较于原始MobileNetV2，准确率提高了0.083．与AlexNet、VggNet16、ResNet50、ConvNeXt等网络相比，3D ELD_MobileNetV2表现最佳，准确率为0.792，F1分数（F1_Score）为0.688、0.750、0.848、0.872，微平均曲线下面积（AUC）为0.954，宏平均AUC为0.948．该研究所提出的模型能够较好对不同时期的肝结节进行分类，有望为肝癌早期诊断提供帮助．

The morbidity and mortality rates of liver cancer in China remain concerning, emphasizing the urgent need for early diagnosis and treatment to improve the situation. In response to this challenge, we propose a novel 3D ELD_MobileNetV2 hepatic nodule classification model that incorporates attention mechanism and dilated convolution. This model is specifically designed to classify abdominal dynamic enhanced magnetic resonance images. Firstly, the two-dimensional network structure was extended to three dimensions to avoid the loss of spatial information during feature extraction of magnetic resonance imaging (MRI). Secondly, based on the local cross-channel interaction strategy, an efficient channel attention mechanism combining local features and global features was embedded in the bottleneck structure of MobileNetV2 network to enhance the key feature extraction capability. Then, 3D dilated structure was introduced into depthwise convolution to improve the receptive field of the convolution kernel. Meanwhile, the original activation function was replaced with Leaky ReLU6 activation function to improve the model's robustness. The model was tested and validated on a dataset comprising 120 patients (30 cases each of hepatitis, cirrhotic nodules, dysplastic nodules, and hepatocellular carcinoma). Experimental results demonstrate significant improvements over the original MobileNetV2, with an accuracy increase of 0.083. Compared to other networks, including AlexNet, VggNet16, ResNet50, ConvNeXt, the 3D ELD_MobileNetV2 achieves superior performance, with the accuracy of 0.792, F1_Score of 0.688, 0.750, 0.848, 0.872, micro-average AUC of 0.954, and macro-average AUC of 0.948. The findings highlight the effectiveness of the proposed model in classifying liver nodules across different stages. This advancement is expected to facilitate early diagnosis of liver cancer and improve clinical outcomes.