Intelligent diagnosis of psoriasis vulgaris based on deep learning and improved fuzzy KMeans
10.3969/j.issn.1005-202X.2024.02.020
- VernacularTitle:基于深度学习及改进模糊KMeans的寻常型银屑病智能诊断方法
- Author:
Liping SHI
1
;
Xiaoqing DU
;
Jing LI
;
Lijuan LIU
;
Guoqiang ZHANG
Author Information
1. 河北医科大学第一医院皮肤科,河北石家庄 050000
- Keywords:
psoriasis vulgaris;
improved fuzzy KMeans clustering algorithm;
Visual Geometry Group 13;
deep convolutional neural network
- From:
Chinese Journal of Medical Physics
2024;41(2):253-257
- CountryChina
- Language:Chinese
-
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.
