改进残差网络的猴痘病毒皮肤病变分类

目前猴痘病毒在全球范围内传播,这种病毒在临床上与其他皮肤疾病难以区分,特别是天花病毒和水痘病毒.在确定性聚合酶链式反应技术和其他生物检测技术还没有完全成熟的情况下,通过计算机辅助诊断技术检测猴痘病毒皮肤病变是一种可行的方法,因此提出了一种基于残差网络的猴痘病毒皮肤病变分类算法.该算法以残差网络为基本框架,结合深度可分离卷积和轻量化注意力,在降低模型计算量与复杂度的同时,也提高了模型的分类性能.实验结果表明,该算法对猴痘病毒皮肤病变表现出较好的分类性能,对猴痘皮肤病变的分类准确率、召回率和精度分别为97.3%, 96.8%和97.2%,且均优于实验中所对比的常见分类模型和其他研究方法.

Classification of Monkeypox Virus Skin Lesions Based on Improved ResNet

Monkeypox virus is currently circulating globally and is clinically indistinguishable from other skin diseases, particularly the smallpox virus and chickenpox virus. In the case that deterministic polymerase chain reaction technology and other biological detection technologies are not fully mature, it is a feasible method to detect skin lesions caused by the monkeypox virus by computer-aided diagnostic technology, so a classification algorithm for skin lesions caused by the monkeypox virus based on the residual network is proposed. Based on the residual network, the algorithm combines deep separable convolution and lightweight attention, which reduces the computational amount and complexity of the model and improves the classification performance of the model. The experimental results show that the algorithm shows excellent classification performance for skin lesions caused by the monkeypox virus, and the classification accuracy, recall, and precision of skin lesions caused by the monkeypox virus are 97.3%, 96.8%, and 97.2%, respectively, which are better than those of the common classification models and other research methods used in the experiment.