# 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
\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{4-6}  & & & & **CPD-10** & \begin{tabular}{c} **CPD-10** \\ **(RDA-SSR)** \\ \end{tabular} \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Cascade R-CNN \\ \end{tabular} } & \multirow{8}{*}{Cross-Entropy} & Swin-T & — & 0.538 & 0.577 \\  & & PVTv2-B2 & — & 0.549 & 0.590 \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=1\) & 0.545 (+0.007) & 0.582 (+0.005) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=2\) & 0.549 (+0.011) & 0.583 (+0.006) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=3\) & 0.551 (+0.013) & 0.589 (+0.012) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=4\) & 0.548 (+0.010) & 0.581 (+0.004) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=1\) & 0.565 (+0.016) & 0.599 (+0.009) \\ \cline{1-1} \cline{3-6}  & & PVTv2-B2 & \(k_{max}=2\) & 0.568 (+0.019) & 0.610 (+0.020) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=3\) & 0.570 (+0.021) & 0.605 (+0.015) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=4\) & 0.557 (+0.008) & 0.597 (+0.007) \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Dynamic R-CNN \\ \end{tabular} } & \multirow{8}{*}{Cross-Entropy} & ResNet-50 & — & 0.465 & 0.528 \\  & & PVTv2-B2 & — & 0.536 & 0.582 \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=1\) & 0.471 (+0.006) & 0.539 (+0.011) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=2\) & 0.483 (+0.018) & 0.540 (+0.012) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=3\) & 0.485 (+0.020) & 0.544 (+0.016) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=4\) & 0.472 (+0.007) & 0.531 (+0.003) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=1\) & 0.556 (+0.020) & 0.594 (+0.012) \\ \cline{1-1} \cline{3-6}  & & PVTv2-B2 & \(k_{max}=2\) & 0.563 (+0.027) & 0.599 (+0.017) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=3\) & 0.567 (+0.031) & 0.604 (+0.022) \\ \cline{1-1} \cline{3-6}  & & & \(k_{max}=4\) & 0.558 (+0.022) & 0.595 (+0.013) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._\(k_{max}\), we conduct experiments on the PASCAL VOC dataset, utilizing the VOC2007 training set, and evaluate the results on the VOC2007 test set. Additionally, we demonstrate the efficacy of the DK_Loss loss function. As shown in Table 10, when the parameter \(k_{max}=3\) is set in the DK_Loss loss function, the mAP improves by 1.4% for the Faster R-CNN, 1.9% for the Cascade R-CNN, and 1.6% for the Dynamic R-CNN, which is the current optimal, but taken as 4, the performance decreases instead, primarily because the DK_Loss loss function makes the model focus more on learning difficult samples, by increasing the loss contribution of difficult samples. If the value of \(dk_{t}\) is not restricted with \(k_{max}\), the focus of the model will always be on the abnormal hard-to-classify samples, which may not be correctly identified for other reasons, and mislead the subsequent optimization direction of the model. Therefore, \(k_{max}\) is not as large as possible, but depends on the number of misclassification categories of most normal samples in the dataset, making the model focus on the global situation of the distribution of normal difficult samples rather than extreme data. Additionally, the \(k_{max}\)'s value mainly depends on the likelihood of the misclassification category number for most samples. For example, for the PASCAL VOC2007 dataset, the confusion matrix of the model trained using the Cascade R-CNN is shown in Figure 10. It can be seen that most of the category confusion category numbers are less than or equal to 3, Consistent with the experimental results, so the proposed value of \(k_{max}\) is set to 3. The similarity between the conclusion and the CPD-10 dataset results validates the validity and generalizability of the DK_Loss loss function.