Characterization of Covid-19 Dataset using Complex Networks and Image Processing

TL;DR

This study analyzes Covid-19 medical image data using complex network models and image processing techniques to uncover hidden patterns that differentiate positive and negative cases.

Contribution

It introduces a novel approach combining statistical and GLCM features with complex network analysis to characterize Covid-19 datasets.

Findings

Hidden patterns are detectable in Covid-19 images using complex networks.

Complex network analysis reveals class-specific structures.

Features extracted can aid in improved classification of Covid-19 cases.

Abstract

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.