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Optimized Convolutional Neural Network for Automatic Detection of COVID-19

K. Muthumayil1, M. Buvana2, K. R. Sekar3, Adnen El Amraoui4,*, Issam Nouaouri4, Romany F. Mansour5

1 Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, 624622, Tamilnadu, India
2 Department of Computer Science & Engineering, PSNA College of Engineering and Technology, Dindigul, 624622, India
3 School of Computing, SASTRA Deemed University, Thanjavur, 613401, India
4 Univ. Artois, U. R. 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), F-62400, Béthune, France
5 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

* Corresponding Author: Adnen El Amraoui. Email: email

Computers, Materials & Continua 2022, 70(1), 1159-1175. https://doi.org/10.32604/cmc.2022.017178

Abstract

The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing followed by CNN-based feature extraction. Then, Multi-objective Black Widow Optimization (MBWO) technique is applied to fine tune the hyperparameters of CNN. Finally, Extreme Learning Machine with autoencoder (ELM-AE) is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels. The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques. The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%. The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.

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APA Style
Muthumayil, K., Buvana, M., Sekar, K.R., Amraoui, A.E., Nouaouri, I. et al. (2022). Optimized convolutional neural network for automatic detection of COVID-19. Computers, Materials & Continua, 70(1), 1159-1175. https://doi.org/10.32604/cmc.2022.017178
Vancouver Style
Muthumayil K, Buvana M, Sekar KR, Amraoui AE, Nouaouri I, Mansour RF. Optimized convolutional neural network for automatic detection of COVID-19. Comput Mater Contin. 2022;70(1):1159-1175 https://doi.org/10.32604/cmc.2022.017178
IEEE Style
K. Muthumayil, M. Buvana, K.R. Sekar, A.E. Amraoui, I. Nouaouri, and R.F. Mansour, “Optimized Convolutional Neural Network for Automatic Detection of COVID-19,” Comput. Mater. Contin., vol. 70, no. 1, pp. 1159-1175, 2022. https://doi.org/10.32604/cmc.2022.017178

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cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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