## 5 Conclusions  
To assess psoriasis severity, the Soriasis Area Severity Index (PASI), composed of erythema, area, desquamation, and induration, is accepted as a golden standard. Actually, the area factor is more important among the PASI parameters to evaluate the psoriasis severity according to physicians' evaluations. Furthermore, these clinical skin images captured in outpatient rooms are often unconstrained. To efficiently measure the area factor, an efficient instance segmentation scheme based on deep convolutional neural networks was proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the YOLACT network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head was utilized to deal with psoriasis images. The backbone network was used to extract feature maps from an image, and FPN was designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head was exploited to predict the  
Figure 12: Instance segmentation result for local regions: (**a1**,**a2**) Input, (**b1**,**b2**) ground truth, (**c1**,**c2**) proposed, and (**d1**,**d2**) Mask R-CNN.  
classification information and bounding box information of objects and mask coefficients. Some prototypes generated by Protonet were combined with mask coefficients to estimate the pixel-level shapes for objects. To achieve instance segmentation for unconstrained psoriasis images, YOLACT++ with a pretrained model was retrained via transfer learning.  
To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels were collected for testing. For objective evaluation, some performance indices, including recall, precision, accuracy, F1-score, and mAP values, are adopted. As for subjective testing, the psoriasis region and normal skin areas could be located and classified well. For testing images, the four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the mAP rates were at least 85.9% after cross validation. As for efficiency, the FPS rate of the proposed scheme reached up to 15. In addition, the F1_score and the execution speed of the proposed scheme were higher than those of the Mask R-CNN-based method. These experimental results demonstrate that the proposed scheme based on YOLACT++ and transfer learning can not only locate the psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme is superior to the Mask R-CNN-based method in terms of the F1_score and the execution speed for dealing with unconstrained psoriasis images.  
Conceptualization, G.-S.L., J.-Y.L. and S.-K.C.; Data curation, K.-T.L. and J.-M.S.; Formal analysis, J.-Y.L. and S.-K.C.; Methodology, G.-S.L.; Software, K.-T.L. and J.-M.S.; Supervision, G.-S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Ministry of Science and Technology (MOST), Taiwan, under the Grants MOST 107-2221-E-167-031-MY2.  
The goal of this study was approved by Institutional Review Board of China Medical University Hospital (IRB appraisal number: CMUH107-REC2-062), Taiwan.  
Patient consent was waived due to de-identification of clinical psoriasis images.  
Please contact authors for data requests.  
The authors wish to acknowledge the help of [PERSON] at China Medical University Hospital, Taiwan, on this study.  
The authors declare no conflict of interest.