为了解决寻常型银屑病在样本分布不平衡的数据中可能会导致的深度学习模型诊断效果下降等问题，通过结合改进模糊KMeans聚类算法对高聚类复杂度数据的处理能力以及Visual Geometry Group 13(VGG13)深度卷积神经网络模型的预测能力，提出一种基于改进模糊KMeans聚类算法的VGG13深度卷积神经网络(VGG13-KMeans)模型，并将其应用于寻常型银屑病的诊断任务中。实验结果表明，相较于VGG13以及ResNet18两种方法，本文方法更适用于对银屑病特征的识别。
Intelligent diagnosis of psoriasis vulgaris based on deep learning and improved fuzzy KMeans
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.
万方数据
维普
