An intelligent COVID-19 classification model using optimal grey-level co-occurrence matrix features with extreme learning machine

Paruchuri, Pavan Kumar Gomathy V.; Devi, E. Anna Sankhwar Shweta Lakshmanaprabu S. K.

In recent times, earlier diagnosis of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Since the chest computed tomography (CT) image diagnosis requires medical experts and more time, an automated intelligent model needs to be developed for effective COVID-19 diagnosis. This paper presents a new automated COVID-19 diagnosis model using optimal grey level co-occurrence matrix (GLCM) based feature extraction and Extreme Learning Machine (ELM) based classification. The input chest images undergo pre-processing to improve the image quality. Next, the optimal GLCM features are derived by the use of Elephant Herd Optimisation (EHO) algorithm. Then, the ELM model is applied to perform the classification task. The performance of the OGLCM-ELM model has been validated using the benchmark dataset and the experimental outcome ensured the superior performance of the proposed model over the compared methods. The proposed OGLCM-ELM model has achieved maximum sensitivity of 89.56%, specificity of 90.45%, F-score of 90.13% and accuracy of 90.69%.