Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis
Pengpeng Pi, Dimas Lima
Abstract
Chest CT is considered to be a more accurate method for diagnosing suspected patients. However, with the spread of the epidemic, traditional diagnostic methods have been unable to meet the requirements of efficiency and speed. Therefore, it is necessary to use artificial intelligence to help people make efficient and accurate judgments. A number of studies have shown that it is feasible to use deep learning methods to help people diagnose COVID-19. However, most of the existing methods are single-layer neural network structures, and their accuracy and efficiency need to be improved. In this scheme, a hybrid model is adopted. Firstly, the gray co-occurrence matrix is used to extract the features of the images, and then the extreme learning machine is used for classification. The experimental results show that the model proposed in this paper is feasible and can help medical staff to accurately determine suspected patients for subsequent isolation and treatment.