# DkFD: Optimizing Common Pediatric Dermatoses Detection with Novel Loss Function and Post-Processing
## 5 Experiments and Discussion
### Experimental Results and Analysis
#### 5.4.3 DK_Loss  
There are 10 diseases involved in the CPD-10 dataset, and it has similarities and variations within each class that affect how challenging it is to diagnose different diseases. We developed the DK_Loss loss function to address this issue and enable the two-stage object recognition algorithm to concentrate more on the difficult-to-classify samples. We compare DK_Loss with the frequently used cross-entropy loss function in the two-stage object identification algorithm. The evaluation is conducted over the CPD-10 original dataset and the CPD-10 augmented dataset, which was created through online data augmentation and Selective Super-Resolution Reconstruction, respectively. When \(k_{max}=1\), only the impact of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **Pre-Processing** & **mAP** \\ \hline \multirow{8}{*}{Faster R-CNN} & \multirow{2}{*}{ResNet-50} & RDA & 0.522 \\ \cline{3-4}  & & & RDA-SSR & 0.535 \\ \cline{3-4}  & & & (+0.013) \\ \cline{2-4}  & \multirow{2}{*}{ConvNeXt-T} & RDA & 0.542 \\ \cline{3-4}  & & & RDA-SSR & 0.558 \\ \cline{3-4}  & & & (+0.016) \\ \cline{2-4}  & \multirow{2}{*}{Swin-T} & RDA & 0.566 \\ \cline{3-4}  & & & (+0.014) \\ \cline{2-4}  & & & RDA & 0.509 \\ \cline{2-4}  & \multirow{2}{*}{Res2 Net-101} & RDA-SSR & 0.536 \\ \cline{3-4}  & & & (+0.027) \\ \hline \multirow{2}{*}{ResNeXt-101} & RDA & 0.534 \\ \cline{2-4}  & & & RDA-SSR & 0.545 \\ \cline{2-4}  & & & (+0.011) \\ \hline \multirow{2}{*}{PVTv2-B2} & RDA & 0.583 \\ \cline{2-4}  & & & (+0.019) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of experimental results with Selective Super-Resolution Reconstruction, RDA in the table represents Random Online Data Augmentation, and SSR represents Selective Super-Resolution Reconstruction. All the results are the best of 5 runs. the coefficients \(1-q(x_{t})\) is verified. Various values for \(k_{max}\) are established and compared, and the optimal parameter values for \(k_{max}\) are then determined. In Table 9, the testing findings are displayed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Loss Function**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**mAP**} \\ \cline{5-6}  & & & & **CPD-10** & \multicolumn{2}{c}{**CPD-10**} \\  & & & & **(RDA-SSR)** \\ \hline \multirow{8}{*}{Faster R-CNN} & \multirow{8}{*}{Cross-Entropy} & ResNeXt-101 & — & 0.497 & 0.545 \\  & & Swin-T & — & 0.542 & 0.580 \\  & & PVTv2-B2 & — & 0.549 & 0.602 \\ \cline{2-6}  & & & \(k_{max}=1\) & 0.510 (+0.013) & 0.551 (+0.006) \\ \cline{2-6}  & & ResNeXt-101 & \(k_{max}=2\) & 0.512 (+0.015) & 0.552 (+0.007) \\ \cline{2-6}  & & & \(k_{max}=3\) & 0.517 (+0.020) & 0.554 (+0.009) \\ \cline{2-6}  & & & \(k_{max}=4\) & 0.521 (+0.024) & 0.556 (+0.011) \\ \cline{2-6}  & & & \(k_{max}=1\) & 0.550 (+0.008) & 0.585 (+0.005) \\ \cline{2-6}  & & Swin-T & \(k_{max}=2\) & 0.553 (+0.011) & 0.591 (+0.011) \\ \cline{2-6}  & & & \(k_{max}=3\) & 0.562 (+0.020) & 0.590 (+0.010) \\ \cline{2-6}  & & & \(k_{max}=4\) & 0.560 (+0.018) & 0.588 (+0.008) \\ \cline{2-6}  & & & \(k_{max}=1\) & 0.562 (+0.013) & 0.613 (+0.011) \\ \cline{2-6}  & & PVTv2-B2 & \(k_{max}=2\) & 0.570 (+0.021) & 0.619 (+0.017) \\ \cline{2-6}  & & & \(k_{max}=3\) & 0.576 (+0.027) & 0.622 (+0.020) \\ \cline{2-6}  & & & \(k_{max}=4\) & 0.572 (+0.023) & 0.617 (+0.015) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison over CPD-10 original dataset and augmented dataset based on different loss functions, where CPD-10 (RDA-SSR) represents the augmented dataset of CPD-10. All the results are the best of 5 runs. Figure 9: Comparison of model detection precision. Combining the experimental results of DK Loss on the original CPD-10 dataset and the augmented dataset in Table 9, we can observe that when \(k_{max}=1\), i.e., only increasing the coefficient \(1-q(x_{t})\), the mAP of the model can be improved, which has a positive influence on training.