【摘要】 目的提出一种基于深度残差网络的银屑病分类诊断模型,该模型采用深度学习技术来对银屑病进行分类诊断,有助于减轻医生负担、简化诊疗流程、提高诊断质量。方法首先采用数据增强、银屑病图片大小调整和TFRecord编码等技术对银屑病数据进行预处理后作为模型的输入,然后设计了一个34层的深度残差网络(ResNet-34)来提取银屑病的特征。最后,利用Adam算法作为优化器来对ResNet-34进行训练,并采用交叉熵作为ResNet-34的损失函数来衡量模型的准确性,从而得到一个优化的ResNet-34模型用于银屑病诊断。结果基于K折交叉验证的实验结果表明,所提模型在召回率、F1值和ROC曲线方面的性能要优于其他诊断方法。结论 ResNet-34模型可以实现银屑病的精准诊断,为银屑病数据分析、疾病智能诊治提供技术支持。更多还原

【Abstract】 Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper. Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors, simplify the diagnosis and treatment process, and improve the quality of diagnosis.Methods Firstly, data enhancement, image resizings, and TFRecord coding are used to preprocess the input of the model,and then a 34-layer deep residual network(ResNet-34) is constructed to extract the characteristics of psoriasis. Finally, we used the Adam algorithm as the optimizer to train ResNet-34,used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model, and obtained an optimized ResNet-34 model for psoriasis diagnosis.Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate, F1-score and ROC curve.Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis, and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.更多还原