Abstract

Info

Title:: Intelligent diagnosis of psoriasis vulgaris based on deep learning and improved fuzzy KMeans

Author(s):: SHI Liping1; DU Xiaoqing2; LI Jing1; LIU Lijuan1; ZHANG Guoqiang1; 1. Department of Dermatology, the First Hospital of Hebei Medical University, Shijiazhuang 050000, China 2. Department of Dermatology, the 980th Hospital of PLA Joint Logistic Support Force, Shijiazhuang 050000, China

Keywords:: Keywords: psoriasis vulgaris improved fuzzy KMeans clustering algorithm Visual Geometry Group 13 deep convolutional neural network

Abstract:: Abstract: In order to address issues such as the decline in diagnostic performance of deep learning models due to imbalanced data distribution in psoriasis vulgaris, a VGG13-based deep convolutional neural network model is proposed by integrating the processing capability of the improved fuzzy KMeans clustering algorithm for highly clustered complex data and the predictive capability of VGG13 deep convolutional neural network model. The model is applied to the diagnosis of psoriasis vulgaris, and the experimental results indicate that compared with VGG13 and resNet18, the proposed approach based on deep learning and improved fuzzy KMeans is more suitable for identifying psoriasis features.