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

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

Addresses: Department of Computer Science and Engineering, Faculty of Science and Technology, IFHE, Hyderabad, Telangana, India ' Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India ' Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India ' Department of Electronics and Instrumentation Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract: 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%.

Keywords: COVID-19; disease diagnosis; feature extraction; classification; deep learning.

DOI: 10.1504/IJCAT.2021.117275

International Journal of Computer Applications in Technology, 2021 Vol.65 No.4, pp.334 - 342

Received: 14 Jun 2020
Accepted: 13 Aug 2020

Published online: 31 Aug 2021 *

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