Letter
Modification of the nail psoriasis severity index

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    Convolutional deep neural network (CNN) performs promisingly in object recognition and image classification [8]. Mask R-CNN [9]; an extension on faster Region-based CNN (R-CNN), allows efficient object detection in image and generate segmentation mask for each instance at the same time. Connected to a Feature Pyramid Network (FPN) and a ResNet101 backbone, Mask R-CNN uses ResNet101 architecture to extract features from the input image and a regional proposal network (RPN) to predicts if the object is present in that region.

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    Consequently, the mtNAPSI was used to measure the severity of psoriasis in the nail matrix and nail bed for the most severely affected nail. Each nail quadrant was scored from 0 (no sign) to 3 (severe involvement), providing a maximum score of 96 (Parrish et al. 2005). The subject global assessment (SGA) and physician global assessment (PGA) were performed at week 12 to measure the improvement of nail psoriasis.

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