# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 4 Experiments and Analysis
### Ablation Experiments
Overall, the highest classification metrics were achieved on all three datasets when setting the M2C module to stage 4 and the Rep-C module to stage 6. Therefore, we adopt this combination as the design rule for the Eff-PCNet architecture. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **M2C** & **Rep-C** & **Acc** & **F1** & **Precision** & **Recall** & **Auc** \\ \hline \multirow{4}{*}{HAM10000} & ✗ & ✗ & 0.8637 & 0.7522 & 0.7788 & 0.7349 & 0.8492 \\  & ✗ & ✗ & 0.8695 & 0.7523 & 0.7474 & 0.7658 & 0.8673 \\  & ✗ & ✗ & 0.8705 & 0.7651 & 0.7905 & 0.7472 & 0.8568 \\  & ✗ & ✗ & 0.8740 & 0.7744 & 0.7845 & 0.7669 & 0.8664 \\ \hline \multirow{4}{*}{SkinCancer} & ✗ & ✗ & 0.8970 & 0.8962 & 0.8958 & 0.8967 & 0.8967 \\  & ✗ &� & 0.8985 & 0.8980 & 0.8972 & 0.8994 & 0.8994 \\  & ✗ & ✗ & 0.9076 & 0.9071 & 0.9063 & 0.9083 & 0.9083 \\  & ✗ & ✓ & 0.9106 & 0.9102 & 0.9094 & 0.9117 & 0.9117 \\ \hline \multirow{4}{*}{Chest-Xray} & ✗ & ✗ & **0.9625** & 0.9700 & 0.9652 & 0.9749 & 0.9585 \\  & ✗ & ✗ & 0.9625 & 0.9704 & 0.9539 & 0.9874 & 0.9545 \\ \cline{1-1}  & ✗ & ✓ & 0.9688 & 0.9752 & 0.9610 & 0.9899 & 0.9619 \\ \cline{1-1}  & ✗ & ✓ & 0.9703 & 0.9766 & 0.9588 & 0.9950 & 0.9624 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation experiments for each module, where ‘✗’ means that the module was not added and ‘✓’ means that it was added. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **M2C** & **Rep-C** & **Acc** & **F1** & **Precision** & **Recall** & **Auc** \\ \hline \multirow{4}{*}{HAM10000} & Baseline & Baseline & 0.8637 & 0.7522 & 0.7788 & 0.7349 & 0.8492 \\  & 3 & 0 & 0.8615 & 0.7295 & 0.7289 & 0.7384 & 0.8507 \\  & 7 & 0 & 0.8655 & 0.7379 & 0.7459 & 0.7332 & 0.8498 \\  & 0 & 3 & 0.8625 & 0.7293 & 0.7459 & 0.7207 & 0.8420 \\  & 0 & 7 & 0.8690 & 0.7592 & 0.7832 & 0.7400 & 0.8521 \\  & Eff-PCNet & Eff-PCNet & 0.8740 & 0.7744 & 0.7845 & 0.7669 & 0.8664 \\ \hline \multirow{4}{*}{SkinCancer} & Baseline & Baseline & 0.8970 & 0.8962 & 0.8958 & 0.8967 & 0.8967 \\  & 3 & 0 & 0.8788 & 0.8776 & 0.8783 & 0.8769 & 0.8769 \\  & 7 & 0 & 0.8924 & 0.8917 & 0.8912 & 0.8922 & 0.8922 \\  & 0 & 3 & 0.8833 & 0.8824 & 0.8823 & 0.8825 & 0.8825 \\  & 0 & 7 & 0.900 & 0.8990 & 0.8998 & 0.8983 & 0.8983 \\  & Eff-PCNet & Eff-PCNet & 0.9106 & 0.9102 & 0.9094 & 0.9117 & 0.9117 \\ \hline \multirow{4}{*}{Chest-Xray} & Baseline & Baseline & 0.9625 & 0.9700 & 0.9652 & 0.9749 & 0.9585 \\  & 3 & 0 & 0.9625 & 0.9701 & 0.9629 & 0.9774 & 0.9577 \\ \cline{1-1}  & 7 & 0 & 0.9625 & 0.9704 & 0.9539 & 0.9874 & 0.9545 \\ \cline{1-1}  & 0 & 3 & 0.9594 & 0.9678 & 0.9537 & 0.9824 & 0.9519 \\ \cline{1-1}  & 0 & 7 & 0.9625 & 0.9704 & 0.9539 & 0.9874 & 0.9545 \\ \cline{1-1}  & Eff-PCNet & Eff-PCNet & 0.9703 & 0.9766 & 0.9588 & 0.9950 & 0.9624 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Variation of module positions for ablation experiments: 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 stage3, and 7 means that the M2C module or Rep-C module is stage 7.