Optimization of psoriasis assessment system based on patch images

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.

Deep Learning Application for Effective Classification of Different Types of Psoriasis

“…In another system, a psoriasis assessment system was proposed using algorithms including KNN, random forest (RF), deep neural network (DNN), Naïve Bayes, and SVM. A total of 80 psoriasis patch images were used, and the results demonstrated the highest accuracies of 98.6% and 92.6% achieved via RF and KNN, respectively [ 20 ]. In a similar study, Dash et al [ 21 ] proposed a CNN model for the detection of psoriasis.…”

“…Various deep learning approaches have been used in dermatology to predict and classify skin problems with high accuracy. For categorizing skin images for the identification of skin lesions, such as malignant melanoma, basal cell carcinoma, actinic keratosis, squamous cell carcinoma, and psoriasis, skin analysis algorithms have been developed employing Mask RCNN, transfer learning, and CNN frameworks [ 14 – 20 ]. All of these methods entail the classification of a single kind of skin condition.…”

Automatic detection and evaluation of nail psoriasis based on deep learning: a preliminary application and exploration

“…According to surveys, skin health affects 30-70% 1 of people in the world, and constitutes a heavy burden on global health. Psoriasis is a chronic inflammatory skin disease that occurs throughout the body in various forms and is associated with diseases such as heart disease, diabetes, and depression 2 . According to statistics, more than 125 million patients with psoriasis have been recorded worldwide, with a prevalence rate of 1-3% 3 .…”

“…Shrivastava et al 12 designed of a segmentation system by Bayesian and developed a psoriasis risk assessment system. Shakir et al 13 Due to the shortcomings of traditional machine learning methods that are unreliable and poorly robust, deep learning methods are becoming more and more popular in psoriasis CADs, including classification 14 , lesion segmentation 15 and multitasking 2,16 . Peng et al 14 constructed a psoriasis classification diagnosis model based on ResNet-34.…”

“…Raj et al 17 designed a customized and compact U-Net architecture for segmentation of psoriasis lesions. Dash et al 16 and Moon et al 2 respectively constructed two models to do segmentation and classification tasks. Zhao et al 18 designed a two-stage deep neural network to identify psoriasis.…”