###### Abstract  
In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficientS (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head is used to predict the classification information, bounding box information, and mask coefficients of objects. Some prototypes generated by Protonet are 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 is retrained via transfer learning. To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels are collected for testing. As for subjective testing, the psoriasis regions and normal skin areas can be located and classified well. The four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the Mean Average Precision (mAP) rates of the proposed scheme were at least 85.9% after cross validation. As for efficiency, the frames per second (FFS) 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 Region-Based Convolutional Neural Networks (R-CNN)-based method. These results show that the proposed scheme based on YOLACT++ can not only detect psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme outperforms the Mask R-CNN-based method for unconstrained psoriasis images. instance segmentation; deep convolutional neural network; computer-aided diagnosis; YOLACT++; Mask R-CNN +
Footnote †: journal: Applied Sciences  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 2 and [PERSON] 3**  
**Featureized Application: The proposed scheme can be utilized to develop a computer aided diagnosis software for assessing psoriasis severity via mobile devices. **  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Instance Segmentation Based on Deep Convolutional Neural Networks and Transfer Learning for Unconstrained Psoriasis Skin Images. _Appl. Sci._**2021**, _11_, 3155. Figure 1 illustrates some psoriasis images taken in outpatient rooms. As shown in Figure 1, the symptoms of patients with psoriasis are erythema, desquamation, and induration. In addition, the psoriasis regions shown in Figure 1 have different sizes and arbitrary shapes. There is no doubt that observing and evaluating the patients' skin to obtain PASI scores is difficult for physicians. On the other hand, the area factor is more important among the PASI parameters to evaluate the psoriasis severity according to physicians' evaluations. For instance, the ratio of psoriasis area to total skin surface less than 10% could be considered as the mild-level severity. Therefore, efficiently measuring the ratio of psoriasis areas to total skin surface is very helpful to assess the psoriasis severity for physicians. In dermatology, the symptoms of psoriasis diseases appearing on the skin surfaces can be recorded using digital devices with cameras. It is expected that an image-based computer-aided diagnosis (CAD) developed to analyze psoriasis images can reduce the working load of physicians and obtain a consistent and efficient assessment. To develop an image-based CAD method for clinical psoriasis images, image segmentation is a key process for further analyses [2; 3; 4; 5; 6].