# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 4 Experiments and Analysis
### Ablation Experiments  
In this section, we conduct two ablation studies on the two modules proposed in this paper, M2C and Rep-C, on three datasets. First, we analyze the effects of the individual M2C and Rep-C modules on the model classification performance, respectively. Then, in order to investigate the effect of the change of position of the M2C and Rep-C modules in Eff-PCNet on the model performance, we similarly conducted an ablation study. Individual contribution of each module: In order to verify the impact of individual M2C and Rep-C modules on the model classification performance, we conducted an ablation study. In this ablation study, we always used the CNN stage in the baseline. The results of the ablation experiments on the three datasets--HAM10000, SkinCancer and Chest-Xray--are shown in Table 7. From the table, we can see that the use of M2C and Rep-C modules alone compared to the baseline has different degrees of improvement on all three datasets, which further proves the effectiveness of our proposed module. Overall, it seems that the Rep-C module alone contributes more than the M2C module on all three datasets. \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 & 0.9625 & 0.9700 & 0.9652 & 0.9749 & 0.9585 \\  & ResNet50 & 23.50 & 4.1 & 0.9547 & 0.9642 & 0.9467 & 0.9824 & 0.9458 \\  & ResNet101 & 42.50 & 7.9 & 0.9516 & 0.9625 & 0.9277 & 1.0000 & 0.9360 \\  & RegNet & 2.32 & 0.21 & 0.9234 & 0.9407 & 0.9068 & 0.9774 & 0.9060 \\  & RepVGG-B2 & 86.50 & 20.5 & 0.9516 & 0.9617 & 0.9400 & 0.9849 & 0.9408 \\  & FasterNet & 13.70 & 1.9 & 0.9438 & 0.9554 & 0.9415 & 0.9698 & 0.9353 \\ \hline \multirow{4}{*}{Transformer} & Vit-B-16 & 86.2 & 16.9 & 0.8891 & 0.9135 & 0.8865 & 0.9422 & 0.8719 \\  & Swin-T & 27.50 & 4.37 & 0.9188 & 0.9370 & 0.9042 & 0.9724 & 0.9015 \\  & Van-b2 & 26.07 & 5.04 & 0.9563 & 0.9654 & 0.9490 & 0.9824 & 0.9478 \\  & Cswin & 23.57 & 3.65 & 0.9375 & 0.9516 & 0.9182 & 0.9874 & 0.9214 \\ \hline \multirow{4}{*}{MLP} & MLP-mixer-B & 59.30 & 3.1 & 0.9266 & 0.9432 & 0.9091 & 0.9799 & 0.9094 \\  & HireMLP-B & 32.60 & 4.26 & 0.9453 & 0.9574 & 0.9291 & 0.9874 & 0.9317 \\  & CycleMLP-B4 & 26.29 & 3.27 & 0.9438 & 0.9660 & 0.9310 & 0.9824 & 0.9313 \\ \hline \multirow{4}{*}{Hybrid method} & P2T & 21.81 & 4.08 & 0.9453 & 0.9566 & 0.9438 & 0.9698 & 0.9374 \\  & ConvMLP & 8.5 & 2.4 & 0.9266 & 0.9436 & 0.9034 & 0.9874 & 0.9060 \\  & NextVit & 30.7 & 5.8 & 0.9563 & 0.9653 & 0.9534 & 0.9774 & 0.9494 \\  & SCNet & 55.1 & 12.1 & 0.9484 & 0.9598 & 0.9314 & 0.9899 & 0.9351 \\ \hline CNN & Eff-PCNet(Ours) & 20.39 & 2.92 & 0.9703 & 0.9766 & 0.9588 & 0.9950 & 0.9624 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of comparison experiments on the Chest-Xray dataset. Figure 10: Grad-CAM visualization results on the Chest-Xray dataset. Variation of M2C module and Eff-rep module positions: In this part, we explore the effect of variation of the M2C module and Rep-C module positions in Eff-PCNet on the model performance. The results of the M2C module and the Rep-C module on the three datasets at different locations of Eff-PCNet are shown in Table 8. In the table, we only list the experimental results for the main locations, where 0 means that the M2C module or Rep-C module is in the same location as the corresponding module in our Eff-PCNet (stage 4 for the M2C module, and stage 6 for the Rep-C module), 3 means that the M2C module or Rep-C module is in the location of stage 3, and 7 means that the M2C module or Rep-C module is stage 7. By comparing the results of the ablation experiments with the M2C and Rep-C modules in different positions, we can see that the combination of the positions of the M2C and Rep-C modules in any way has mixed results on the three datasets, with some combinations having a small enhancement with respect to the baseline and some combinations having a decrease instead.