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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 (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 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.
Highlights
Psoriasis is a skin disease which is a chronic inflammatory skin condition [1,2]
It is expected that You Only Look At CoefficienTs (YOLACT)++ with deformable CNN (DCN) can extract more useful visual feature maps from psoriasis images for further analyses
It is expected that the convolutional layers of a pretrained YOLACT++ based on ImageNet should contain a lot of rich knowledge extracted from natural images
Summary
Psoriasis is a skin disease which is a chronic inflammatory skin condition [1,2]. The appearance of psoriasis on skin causes anxiety and social obstacles for patients. An existing method [12] developed based on a sliding-window approach was proposed for psoriasis images. Some stateof-the-art deep learning-based medical image segmentation methods such as the Fully Convolution Neural Network (FCN) and U-Net have been proposed [11,13,14,15,16,17,18,19,20]. To achieve pixel-wise classification, the fully connected layer is replaced by a fully convolutional layer in FCNs [19,20] Another popular approach is the U-Net, which was developed based on the encoder-decoder structure [13,19,20]. The U-net can achieve semantic segmentation, it cannot distinguish different regions of the same category This means that an instance segmentation scheme is suitable for psoriasis image segmentation compared with semantic segmentation.
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