论文标题
使用复杂的网络和图像处理的COVID-19数据集表征
Characterization of Covid-19 Dataset using Complex Networks and Image Processing
论文作者
论文摘要
本文旨在探讨Covid-19数据集背后的模式结构。该数据集包括阳性和阴性病例的医学图像。选择100个样本的样本，每个班级50个样本。计算直方图频率以使用统计测量值获得特征，除了使用灰度级别共发生矩阵（GLCM）提取特征。使用这两个功能分别构建复杂的网络来分析邻接矩阵并检查模式的存在。初始实验介绍了每个类中数据集中隐藏模式的证据，使用复杂的网络表示可见。
This paper aims to explore the structure of pattern behind covid-19 dataset. The dataset includes medical images with positive and negative cases. A sample of 100 sample is chosen, 50 per each class. An histogram frequency is calculated to get features using statistical measurements, besides a feature extraction using Grey Level Co-Occurrence Matrix (GLCM). Using both features are build Complex Networks respectively to analyze the adjacency matrices and check the presence of patterns. Initial experiments introduces the evidence of hidden patterns in the dataset for each class, which are visible using Complex Networks representation.
