Abstract
Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression. The psoriasis area severity index (PASI) score, a tool used to evaluate the severity of psoriasis, is currently used in clinical trials and clinical research. The determination of severity is based on the subjective judgment of the clinician. Thus, the disease evaluation deviations are induced. Therefore, we propose optimal algorithms that can effectively segment the lesion area and classify the severity. In addition, a new dataset on psoriasis was built, including patch images of erythema and scaling. We performed psoriasis lesion segmentation and classified the disease severity. In addition, we evaluated the best-performing segmentation method and classifier and analyzed features that are highly related to the severity of psoriasis. In conclusion, we presented the optimal techniques for evaluating the severity of psoriasis. Our newly constructed dataset improved the generalization performance of psoriasis diagnosis and evaluation. It proposed an optimal system for specific evaluation indicators of the disease and a quantitative PASI scoring method. The proposed system can help to evaluate the severity of localized psoriasis more accurately.
Highlights
Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression
For a two-dimensional analysis of psoriasis, we focused on the characteristic changes of erythema and scale among the psoriasis area severity index (PASI) score indicators according to severity based on the local region where the disease is distributed
The PASI score is typically used for the diagnosis and evaluation of psoriatic diseases, but this evaluation index includes the subjective opinion of the clinician; there is a disadvantage in that the evaluation result is highly variable and has poor r eproducibility[23]
Summary
Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression. We performed psoriasis lesion segmentation and classified the disease severity. Our newly constructed dataset improved the generalization performance of psoriasis diagnosis and evaluation It proposed an optimal system for specific evaluation indicators of the disease and a quantitative PASI scoring method. Psoriasis is a chronic inflammatory skin disease characterized by papules and plaques covered with silver-white scales It occurs regardless of age or sex. Because the PASI score relies on the subjective evaluation of the clinician, deviations in disease diagnosis may occur. Various computer-aided diagnosis (CADx) systems have been developed to quantify psoriasis disease evaluation[4,5,6,7,8,9] These previous studies have dealt mainly with the segmentation of psoriasis areas and the classification of severity. Classification method that works robustly for various types of psoriatic diseases and attempted to prove the performance of the psoriasis evaluation system
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