Characterization of Covid-19 Dataset using Complex Networks and Image Processing
This work addresses pattern detection in COVID-19 diagnosis for medical imaging, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of identifying hidden patterns in a COVID-19 medical image dataset by constructing complex networks from statistical and GLCM features, finding evidence of distinct patterns for positive and negative cases.
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.