  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.
