Detection of coronavirus disease using texture analysis and machine learning methods. (20th January 2023) - Bourouis, Sami
The recent outbreak of the novel SARS-CoV-2 virus (COVID-19) has caused serious problems across the world. Patients with such diseases can have severe symptoms and may die. The early diagnosis of COVID-19 may reduce the death rate. Chest X-ray technology is one of the good low-cost diagnostic tools in analysing such diseases. However, its accurate detection is becoming prone to serious errors caused by the low radiographic contrast. In this paper, we address the problem of data classification using texture features and machine learning along with using AI algorithms. The aim is to show that it is possible to take advantage of both visual texture descriptors and AI methods to accurately diagnose COVID-19. An evaluation process was conducted on real datasets showing the merits of developed framework. The other aim is to show the robustness of texture features in solving the current problem. We validated the developed models on different datasets and we evaluate their performance in terms of various metrics. Through extensive experiments, we prove the merits of the current work.