## 4 Experimental Results
### Comparison with Mask R-CNN-Based Method  
As mentioned in Section 2, Mask R-CNN is one of the state-of-the-art instance segmentation methods [11; 24]. To make a comparison, we retrained Mask R-CNN for instance psoriasis segmentation. A pr-trained Mask R-CNN model based on the MSCOCO dataset [30] was retrained for psoriasis image segmentation via transfer learning. Figure 7d shows the results of Mask R-CNN. As shown in Figure 7(d2,d3), the boundaries of skin and background may not be located well using Mask R-CNN. Compared with Figure 7c,d, the normal skin regions can be localized and identified well using the proposed scheme. Furthermore, although the input images contain background regions, the proposed scheme can not only detect psoriasis regions but also distinguish the psoriasis regions and the normal skin areas from the background well. Figure 10 illustrates the performance indices of the proposed scheme and the Mask R-CNN-based method. As shown in Figure 10, the average precision, recall, accuracy, and F1-score values of the proposed scheme were 90.88%, 96.14%, 97.30%, and 96.08%, respectively. The results show that the proposed scheme performed instance segmentation well for unconstrained psoriasis images. In addition, the average precision, recall, accuracy, and F1-score values of the Mask R-CNN-based method were 92.83%, 90.88%, 94.08%, and 91.82%, respectively. Compared with the Mask R-CNN-based method, the recall rate was better and the increment of F1_score was 4.2% for psoriasis regions and normal skin areas using the proposed scheme. Therefore, according to the above experimental results, the proposed scheme can provide a better instance segmentation performance compared with the Mask R-CNN-based method for unconstrained psoriasis images. To evaluate the proposed scheme in psoriasis images with different sizes, the test images with 3000 \(\times\) 4000 pixels were tested. Figure 11 illustrates the instance segmentation results of Mask R-CNN and the proposed scheme. Figure 11a,b are the input images and the ground truth, respectively. As shown in Figure 11a,b, the test images had psoriasis regions with different severity levels, different sizes, and arbitrary boundaries. Figure 11c,d are the segmentation results of the proposed scheme and Mask R-CNN, respectively. As we can observe in Figure 11c, the proposed scheme located psoriasis regions with different severity levels and then provided good object segmentation boundaries. There is a false classified region in the bottom of Figure 11(c3). As we can see in Figure 11d, the boundaries between normal skin and background may not be separated well using the Mask-R-CNN-based method. There are false classified regions in the bottom of Figure 11(d2,d3). Compared with Mask R-CNN, the proposed scheme can find the boundaries between normal skin and background well. The main reason is that DCN was adopted in YOLACT++ to enhance the capability of handling objects with arbitrary boundaries and then reduce the effect of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Precision** & **Recall** & **Accuracy** & **F1\_Score** \\ \hline \multirow{3}{*}{Psoriasis} & noise & \multirow{3}{*}{93.05\%} & \multirow{3}{*}{84.37\%} & \multirow{3}{*}{97.67\%} & \multirow{3}{*}{86.87\%} \\  & insertion & & & & \\ \cline{2-5}  & blurring & & & & \\ \hline \multirow{3}{*}{Normal skin} & noise & \multirow{3}{*}{96.32\%} & \multirow{3}{*}{97.55\%} & \multirow{3}{*}{96.89\%} & \multirow{3}{*}{96.87\%} \\  & insertion & & & & \\ \cline{2-5}  & blurring & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Psoriasis + \\ Normal skin \\ \end{tabular} } & noise & \multirow{3}{*}{96.00\%} & \multirow{3}{*}{96.04\%} & \multirow{3}{*}{97.29\%} & \multirow{3}{*}{96.02\%} \\  & insertion & & & & \\ \cline{2-5}  & blurring & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of the proposed scheme for robustness evaluation.