## 3 Experiments and Results
### Performance of the Sub-Networks on Small-Scale Dataset
Compared with Inception-v3 and ResNet50, the improvement of diagnostic performance is limited in the case of DenseNet. Accuracy of the best sub-network of DenseNet121 (\"C5_b6\") is only 0.02% higher than that of complete DenseNet121 (\"C5_b16\"), with 0.07% decline in standard deviation. It can be well explained in terms of architecture different from Inception-v3 and ResNet50, as deep layers of DenseNet121 receive concatenation of all the preceding feature maps, which reserve information of shallow layers in the output. Thus, the removal of the deep layer has little influence on the classification. However, similar performance is obtained with only 78% of trainable parameters, and \"C5_b6\" still beats other sub-networks in DenseNet121. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Average** & **Standard Deviation** & **F** & \(\mathbf{p}\)**-Value** & **F Crit** \\ \hline Mixed6 & 99.67 & 0.08 & & & \\ Mixed10 & 99.27 & 0.41 & 8.984 & 7.73 \(\times\) 10\({}^{-3}\) & 4.414 \\ \hline Ac37 & 99.49 & 0.18 & & & \\ Ac49 & 99.09 & 0.25 & 16.690 & 6.94 \(\times\) 10\({}^{-4}\) & 4.414 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: One-way analysis of variance (ANOVA) with \(\alpha\) = 0.05. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline ScSPM [14] & & 97.75 & 96.67 & 98.30 \\ DL-based CNN [35] & & 98.86 & 98.30 & 99.15 \\ IBDL [9] & & 94.57 & 92.03 & 95.85 \\ \hline \multirow{4}{*}{Sub-networks of Inception-v3} & Mixed6 & 99.67 \(\pm\) 0.08 & 99.50 \(\pm\) 0.12 & 99.75 \(\pm\) 0.06 \\  & Mixed7 & 99.51 \(\pm\) 0.17 & 99.26 \(\pm\) 0.25 & 99.63 \(\pm\) 0.13 \\ Sub-networks of Inception-v3 & Mixed8 & 99.58 \(\pm\) 0.13 & 99.37 \(\pm\) 0.19 & 99.68 \(\pm\) 0.10 \\  & Mixed9 & 99.56 \(\pm\) 0.17 & 99.35 \(\pm\) 0.25 & 99.67 \(\pm\) 0.13 \\  & Mixed10 & 99.27 \(\pm\) 0.41 & 98.90 \(\pm\) 0.61 & 99.45 \(\pm\) 0.31 \\ \hline \multirow{4}{*}{Sub-networks of ResNet50} & Ac37 & 99.49 \(\pm\) 0.18 & 99.24 \(\pm\) 0.26 & 99.62 \(\pm\) 0.13 \\  & Ac40 & 99.35 \(\pm\) 0.19 & 99.02 \(\pm\) 0.28 & 99.51 \(\pm\) 0.14 \\  & Ac43 & 99.34 \(\pm\) 0.19 & 99.01 \(\pm\) 0.29 & 99.50 \(\pm\) 0.14 \\  & Ac46 & 99.27 \(\pm\) 0.19 & 98.90 \(\pm\) 0.28 & 99.45 \(\pm\) 0.14 \\  & Ac49 & 99.09 \(\pm\) 0.25 & 98.64 \(\pm\) 0.38 & 99.32 \(\pm\) 0.19 \\ \hline \multirow{4}{*}{Sub-networks of DenseNet121} & C5\_b2 & 99.53 \(\pm\) 0.16 & 99.30 \(\pm\) 0.24 & 99.65 \(\pm\) 0.12 \\  & C5\_b4 & 99.56 \(\pm\) 0.17 & 99.35 \(\pm\) 0.26 & 99.67 \(\pm\) 0.13 \\  & C5\_b6 & 99.60 \(\pm\) 0.18 & 99.40 \(\pm\) 0.26 & 99.70 \(\pm\) 0.13 \\  & C5\_b8 & 99.53 \(\pm\) 0.18 & 99.30 \(\pm\) 0.27 & 99.65 \(\pm\) 0.13 \\  & C5\_b10 & 99.46 \(\pm\) 0.23 & 99.19 \(\pm\) 0.34 & 99.59 \(\pm\) 0.17 \\  & C5\_b12 & 99.48 \(\pm\) 0.17 & 99.23 \(\pm\) 0.26 & 99.61 \(\pm\) 0.13 \\  & C5\_b14 & 99.55 \(\pm\) 0.16 & 99.33 \(\pm\) 0.24 & 99.67 \(\pm\) 0.12 \\  & C5\_b16 & 99.58 \(\pm\) 0.11 & 99.37 \(\pm\) 0.17 & 99.68 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance (%) of the sub-networks on the clinical dataset. Overall, the removal of deep layers in Inception-v3 and ResNet50 improves the accuracy and stability significantly on the small-scale dataset. Due to the dense connection structure of DenseNet121, a similar improvement does not exist in this case. The best sub-network among them is \"Mixed6\" with accuracy of 99.67% and standard deviation of 0.08%. The number of trainable parameters of \"Mixed6\" is also acceptable compared to other sub-networks.