# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## 4 Experiments
### Model Comparison Experiment  
We examined the performance metrics of various advanced classification techniques on the COVID19-CT dataset. The comparison included VGG-16 [7], ResNet [9], ResNeXt [11], Res2 Net [10], DenseNet [18], EfficientNet [40], ShuffleNet 1.0 \(\times\) (G = 4) [41], SENet [20], GvT [21], and ResGANet [15]. All models were implemented in their original form, sharing the same operational environment and essential hyperparameters. The experimental results are shown in Table 5 and Figure 9.  
As shown in Table 5, the approach introduced in this study surpasses other models in classification tasks, further demonstrating that our method can effectively diagnose COVID19-CT images. The highest values for each metric are indicated in bold. Similar to ResNet and DenseNet, we use residual connections and dense connections to improve the efficiency of feature reuse and gradient flow. ResNet tackles deep network degradation through residual structures, whereas DenseNet achieves efficient feature reuse through dense connectivity. Additionally, like ResNeXt and Res2 Net, we utilize group convolution and multi-scale feature representations to enhance model expressiveness. ResNeXt enhances network width by introducing the concept of cardinality, while Res2 Net captures multi-scale information by building hierarchical residual connections. By incorporating these advantages, we achieve superior classification performance. Making reference to ResGANet, we utilize attention modules to improve the feature representation abilities of convolutional neural networks. ResGANet adaptively recalibrates feature responses using channel and spatial attention mechanisms, whereas EfficientNet balances network depth, width, and resolution through compound scaling. Relative to ResGANet, our network demonstrated a 3.82% enhancement in classification accuracy (79.92% vs. 83.74%). This is mainly attributed to the introduction of the DCAF module and RMT module, which enhance the model's ability to capture multi-scale features and model global contextual information. GvT employs the Talking-Heads mechanism, enhancing model expressiveness through inter-head communication in multi-head attention. However, due to the complex structure of the GvT model, the training process requires more computational resources and time, which may result in its limited performance on small datasets. Our method performs better on small datasets, particularly improving by 0.41% compared to GvT in medical image classification tasks (83.33% vs. 83.74%). Although GvT scores slightly higher than ResGDANet in precision (84.76% vs. 83.46%), ResGDANet performs better in terms of F1 score (83.98% vs. 83.43%), indicating that ResGDANet has an advantage in balancing precision and recall. To further validate the proposed model, we also compared the classification performance of different models on the ISIC2018 dataset. The experimental results are shown in Table 6 and Figure 10.  
The results show that ResGDANet has advantages in medical image classification. In comparison to CBAM and SENet, we built attention modules to enhance the feature representation capability of convolutional neural networks. Our network achieved accuracy improvements of 3.77% over SENet-50 (77.96% vs. 81.73%) and 3.27% over CBAM-50 (78.46% vs. 81.73%). ResGDANet demonstrated a 2.06% accuracy improvement over ResGANet (79.67% vs. 81.73%). These experimental results confirm that incorporating the DCAF module and RMT module or refining features into multiple groups, enhances \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline ResNet & 76.62 & 74.34 & 76.65 & 75.48 \\ SENet & 77.96 & 75.73 & 78.04 & 76.87 \\ CBM & 78.46 & 77.04 & 78.6 & 77.81 \\ ResNeXt & 77.31 & 75.34 & 77.34 & 76.33 \\ Res2 Net & 78.91 & 79.08 & 80.01 & 79.54 \\ ECANet & 78.09 & 76.61 & 78.09 & 77.35 \\ ResGANet & 79.67 & 79.83 & 79.39 & 79.61 \\ ResGDANet & 81.73 & 80.94 & 82.42 & 81.67 \\ \hline \hline \end{tabular}
\end{table}