# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 4 Experiments and Analysis
### Experiments and Analysis
#### 4.4.1 Results of Comparison Experiments on the HAM10000 Dataset  
Table 4 shows the experimental comparison results of Eff-PCNet with some models in HAM10000. We evaluate it with other models in terms of five metrics: Acc, precision, recall, F1, and Auc. At the same time, we also compare the number of parameters and the FLOPs of the models. From the table, we can see that Eff-PCNet has a significant advantage over other competing methods. Eff-PCNet achieves an Acc of 0.874, an F1 of 0.7744, precision of 0.7845, recall of 0.7669, and an Auc of 0.8664. In CNN-based methods such as compared to the classical Resnet50, Eff-PCNet improves Acc by 3.7 percentage points, F1 by 6.31 percentage points, and precision by 10.08 percentage points. Meanwhile, compared with the SOTA FasterNet network, Eff-PCNet improves Acc by 2.75 percentage points, and Auc by 2.87 percentage points, and F1 by 4.86 percentage points. For the Transformer-based method, our Eff-PCNet improves all three metrics: F1, precision, and recall, by more than ten percentage points compared to Vit-B-16. Compared with the MLP-based method, Eff-PCNet exceeds CycleMLP-B4 by 6.3 percentage points in Acc; 6.71 percentage points in Auc; and more than ten percentage points in the remaining three metrics of F1, precision, and recall. In addition, Eff-PCNet improves all three metrics--F1, precision, and recall--by more than nine percentage points over P2T compared to the hybrid model-based model. From the table, it can be seen that our Eff-PCNet maintains high performance, while the number of parameters and FLOPs still maintains a better advantage, which also shows that our method achieves a better trade-off between performance and the number of parameters and computation compared to other methods.  
In addition, Figure 7 shows the Grad-CAM [56] visualization results of some comparison models on the HAM10000 dataset, where column h is the visualization result of our model. From the figure, we can clearly see that our model can better localize the lesion region in the image compared to other models. This further illustrates the better classification performance of our model compared to other models. Finally, we show the confusion matrix of our method on the HAM10000 dataset on the left side of Figure 8, showing the classification of each category separately.  
\begin{table}
\begin{tabular}{c l 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 & 0.8637 & 0.7522 & 0.7788 & 0.7349 & 0.8492 \\  & ResNet50 & 23.5 & 4.1 & 0.837 & 0.7113 & 0.6837 & 0.7453 & 0.8555 \\  & ResNet101 & 42.5 & 7.9 & 0.859 & 0.7218 & 0.7759 & 0.6821 & 0.8206 \\  & RegNet & 2.32 & 0.21 & 0.823 & 0.6669 & 0.6654 & 0.6722 & 0.8145 \\  & RepVGG-B2 & 86.5 & 20.5 & 0.858 & 0.7402 & 0.7627 & 0.7223 & 0.8420 \\  & FasterNet & 13.7 & 1.9 & 0.8465 & 0.7258 & 0.7537 & 0.7144 & 0.8377 \\ \hline \multirow{8}{*}{Transformer} & Vit-B-16 & 86.2 & 16.9 & 0.809 & 0.6335 & 0.6562 & 0.6187 & 0.7845 \\  & Swin-T & 27.50 & 4.37 & 0.8505 & 0.7015 & 0.7202 & 0.6980 & 0.8303 \\  & Van-b2 & 26.07 & 5.04 & 0.8525 & 0.7209 & 0.7358 & 0.7231 & 0.8419 \\  & Cswin & 23.57 & 3.65 & 0.8505 & 0.7375 & 0.7433 & 0.7378 & 0.8509 \\ \hline \multirow{8}{*}{MLP} & MLP-mixer-B & 59.3 & 3.1 & 0.811 & 0.6318 & 0.7015 & 0.5878 & 0.7670 \\  & HireMLP-B & 32.60 & 4.26 & 0.849 & 0.7246 & 0.7241 & 0.7439 & 0.8516 \\  & CycleMLP-B4 & 26.29 & 3.27 & 0.811 & 0.6421 & 0.6462 & 0.6465 & 0.7993 \\ \hline \multirow{8}{*}{Hybrid method} & P2T & 21.81 & 4.08 & 0.836 & 0.6809 & 0.6955 & 0.6736 & 0.8153 \\  & ConvMLP & 8.5 & 2.4 & 0.8450 & 0.7275 & 0.7370 & 0.7240 & 0.8423 \\  & NextVit & 30.7 & 5.8 & 0.8620 & 0.7392 & 0.7426 & 0.7403 & 0.8525 \\  & SCNet & 55.1 & 12.1 & 0.861 & 0.7280 & 0.7519 & 0.7134 & 0.8374 \\ \hline CNN & Eff-PCNet(Ours) & 20.39 & 2.92 & 0.874 & 0.7744 & 0.7845 & 0.7669 & 0.8664 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of comparison experiments on the HAM10000 dataset.