# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 4 Experiments and Analysis
### Experiments and Analysis
#### 4.4.3 Results of Comparison Experiments on the Chest-Xray Dataset  
To further verify that Eff-PCNet has better generalization ability, we conducted experimental validation on the Chest-Xray dataset. Table 6 shows the experimental results of all comparison models on the Chest-Xray dataset. From the table, we can see that the Acc of Eff-PCNet reaches 0.9703, the F1 reaches 0.9766, the precision reaches 0.9588, the recall reaches 0.9950, and the Auc reaches 0.9624. As with the two datasets above, we similarly compare Eff-PCNet with a number of classical, newer methods. On the Chest-Xray dataset, our Eff-PCNet achieves the highest metrics on Acc, F1, and AUC, and precision and recall rank second among all models. Considering the five evaluation metrics, and the number of parameters and FLOPs, our Eff-PCNet still has a clear advantage over other methods. Our method has fewer parameters and computation while achieving high performance. Meanwhile, this lightweight and efficient medical image classification network has a good advantage in practical clinical applications. In addition, Figure 10 demonstrates the Grad-CAM visualization results of different methods on the Chest-Xray dataset, from which  
\begin{table}
\begin{tabular}{c l l l l l l l} \hline \hline  & **Methods** & **Params (M)** & **FLOPs (G)** & **Acc** & **F1** & **Precision** & **Recall** & **Auc** \\ \hline \multirow{8}{*}{CNN} & EfficientNetV2 & 20.18 & 2.87 & 0.8970 & 0.8962 & 0.8958 & 0.8967 & 0.8967 \\  & ResNet50 & 23.5 & 4.1 & 0.8909 & 0.8905 & 0.8899 & 0.8928 & 0.8908 \\  & ResNet101 & 42.5 & 7.9 & 0.8970 & 0.8967 & 0.8963 & 0.8944 & 0.8944 \\  & ResNet & 2.32 & 0.21 & 0.8893 & 0.8888 & 0.8881 & 0.8903 & 0.8903 \\  & RepVGG-B2 & 86.5 & 20.5 & 0.8970 & 0.8964 & 0.8957 & 0.8975 & 0.8975 \\  & FasterNet & 13.7 & 1.9 & 0.8939 & 0.8936 & 0.8931 & 0.8961 & 0.8961 \\ \hline \multirow{4}{*}{Transformer} & Vit-B-16 & 86.2 & 16.9 & 0.8818 & 0.8813 & 0.8806 & 0.8831 & 0.8831 \\  & Swin-T & 27.50 & 4.37 & 0.8894 & 0.8886 & 0.8883 & 0.8889 & 0.8889 \\  & Van-b2 & 26.07 & 5.04 & 0.8864 & 0.8861 & 0.8860 & 0.8892 & 0.8892 \\  & Cswin & 23.57 & 3.65 & 0.8909 & 0.8936 & 0.8930 & 0.8961 & 0.8961 \\ \hline \multirow{4}{*}{MLP} & MLP-mixer-B & 59.3 & 3.1 & 0.8848 & 0.8845 & 0.8840 & 0.8869 & 0.8869 \\  & HireMLP-B & 32.60 & 4.26 & 0.8954 & 0.8947 & 0.8944 & 0.895 & 0.895 \\  & CycleMLP-B4 & 26.29 & 3.27 & 0.8909 & 0.8905 & 0.8899 & 0.8928 & 0.8928 \\ \hline \multirow{4}{*}{Hybrid method} & P2T & 21.81 & 4.08 & 0.8969 & 0.8964 & 0.8957 & 0.8978 & 0.8978 \\  & ConvMLP & 8.5 & 2.4 & 0.8364 & 0.8361 & 0.8373 & 0.8400 & 0.8400 \\  & NextVit & 30.7 & 5.8 & 0.8955 & 0.8948 & 0.8942 & 0.8956 & 0.8956 \\  & SCNet & 55.1 & 12.1 & 0.9060 & 0.9050 & 0.9065 & 0.9039 & 0.9039 \\ \hline CNN & Eff-PCNet(Ours) & 20.39 & 2.92 & 0.9106 & 0.9102 & 0.9094 & 0.9117 & 0.9117 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of comparison experiments on the SkinCancer dataset.  
Figure 9: Grad-CAM visualization results on the SkinCancer dataset.  
we can see that our model is able to localize the chest lesion region better. The middle of Figure 8 demonstrates the confusion matrix visualization of our method on the Chest-Xray dataset, from which it can be observed that Eff-PCNet is better able to correctly classify the category 1.