# DkFD: Optimizing Common Pediatric Dermatoses Detection with Novel Loss Function and Post-Processing
## 5 Experiments and Discussion
### Experimental Results and Analysis
#### 5.4.4 Filter_nms  
The experiments in this section are based on the outcomes of the DK_Loss experiments conducted in the previous section. The evaluation employs the PVTv2-B2 backbone network with different con_thr and cro_thr threshold parameters. First, eliminate the detection boxes for inclusion and contained relationships, and have distinct prediction categories and greater than con_thr confidence score differences. Then, the detection boxes that can be identified as detecting the same lesion region, have distinct prediction categories, and where the difference in confidence scores is greater than the set value cro_thr are deleted. The results of the experiments are presented in Table 11.  
According to the experiment results, the mAP of the model with various con_thr and cro_thr threshold settings is relatively smooth. In addition, when \(con\_thr=0.8\) and \(cro\_thr=0.1\) are set, the best filtering effect is achieved for the misclassified boxes. Setting \(con\_thr=0.8\) and \(cro\_thr=0.1\) and utilizing various post-processing algorithms, Figure 11 compares the detection precision of 10 disease representations with \(con\_thr=0.8\) and \(cro\_thr=0.1\).  
In Figure 11, it is clear that the Filter_nms post-processing algorithm can increase accuracy while largely maintaining mAP. The Filter_nms algorithm can reduce the interference caused by the messy detection boxes and satisfy the requirement of natural image object detection of common pediatric dermatoses, which takes precision as the first criterion, by filtering parts of the detection boxes that are obviously incorrect in prediction categories.  
Simultaneously, we can observe an improvement in the precision of disease detection; however, compared to four diseases, namely insect bite, diaper dermatitis, furtuncle, and pyogenic paronychia, there is no significant improvement. This is because of the fact that, for the screened detection boxes with evident prediction errors, the disease features in the prediction region cannot share a high degree of similarity with other disease features. Otherwise, it is highly probable that multiple prediction categories and their corresponding probabilities do not differ significantly, thereby failing to meet the threshold requirement. There are similarities between hand-foot-and-mouth disease, varicella, and folliculitis, as well as urticaria, atopic dermatitis, and impetigo, among the 10 diseases in the CPD-10  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Cross-Entropy**} & \multicolumn{4}{c}{**DK\_Loss**} \\ \cline{3-6}  & & \(k_{max}=1\) & \(k_{max}=2\) & \(k_{max}=3\) & \(k_{max}=4\) \\ \hline Faster & \multirow{2}{*}{0.775} & 0.783 & 0.787 & 0.789 & 0.787 \\ R-CNN & & (+0.008) & (+0.012) & (+0.014) & (+0.012) \\ \hline Cascade & \multirow{2}{*}{0.763} & 0.778 & 0.780 & 0.782 & 0.780 \\ R-CNN & & (+0.015) & (+0.017) & (+0.019) & (+0.017) \\ \hline Dynamic & \multirow{2}{*}{0.774} & 0.785 & 0.788 & 0.790 & 0.787 \\ R-CNN & & (+0.011) & (+0.014) & (+0.016) & (+0.013) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental results using ResNet-50-FPN backbone on PASCAL VOC2007 test set. All the results are the best of 5 runs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Post-Processing** & **con\_thr** & **cro\_thr** & **mAP** \\ \hline \multirow{5}{*}{Faster R-CNN} & NMS & — & — & 0.622 \\ \cline{2-4}  & & 0.70 & — & 0.620 \\ \cline{2-4}  & & 0.800 & — & 0.622 \\ \cline{2-4}  & \multirow{2}{*}{Filter\_nms} & 0.90 & — & 0.622 \\ \cline{2-4}  & & 0.80 & 0.10 & 0.621 \\ \cline{2-4}  & & 0.80 & 0.20 & 0.621 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of adjustment results of different post-processing methods.  
dataset. Consequently, the detection precision of these six diseases has not substantially improved. Real scenarios of natural images of common pediatric dermatoses after different post-processing methods are evaluated in Figure 12.