Medical imaging plays an indispensable role in precise patient diagnosis. The integration of deep learning into medical diagnostics is becoming increasingly common. However, existing deep learning models face performance and efficiency challenges, especially in resource-constrained scenarios. To overcome these challenges, we introduce a novel dendritic neural efficientnet model called DEN, inspired by the function of brain neurons, which efficiently extracts image features and enhances image classification performance. Assessments on a diabetic retinopathy fundus image dataset reveal DEN’s superior performance compared to EfficientNet and other classical neural network models.

Deep networks are undergoing rapid development. However, as the depth of networks increases, the issue of how to fuse features from different layers becomes increasingly prominent. To address this challenge, we creatively propose a cross-layer feature fusion module based on neural dendrites, termed dendritic learning-based feature fusion (DFF). Compared to other fusion methods, DFF demonstrates superior biological interpretability due to the nonlinear capabilities of dendritic neurons. By integrating the classic ResNet architecture with DFF, we devise the ResNeFt. Benefiting from the unique structure and nonlinear processing capabilities of dendritic neurons, the fused features of ResNeFt exhibit enhanced representational power. Its effectiveness and superiority have been validated on multiple medical datasets.