Psoriasis is a chronic disease, which has affected over 125 million patients around the world. While the nail psoriasis is more common in psoriasis, it is time-consuming and subjective accurately assess the severity of psoriasis. With the development of deep learning and machine learning, more and more automated methods are proposed for the assessment of lesional psoriasis. However, there are few automated methods for accessing nail psoriasis. This paper proposes an automatic evaluation system for nail psoriasis based on deep learning. The system consists of a cascaded neural network, including nail detection model, nail lesion detection model and quadrant classification model, and combined with the scoring algorithm to obtain the nail psoriasis severity index (NAPSI) automatically. On the dataset we built, the mAP of the nail detection model is 0.909, and the accuracy of the quadrant classification model is 0.765. Through the detection of nail lesions with two models, it can be concluded that the mAP of the best model is 0.24. The models and algorithm have been applied and verified in the application of intelligent assessment.
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