Article Dans Une Revue CMC-Computers, Materials & Continua Année : 2022

Optimized Convolutional Neural Network for Automatic Detection of COVID-19

Résumé

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., MBWOCNN technique for diagnosis and classification of COVID-19. MBWOCNN 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.

Dates et versions

hal-04162700 , version 1 (16-07-2023)

Identifiants

Citer

K. Muthumayil, M. Buvana, K. R. Sekar, Adnen El Amraoui, Issam Nouaouri, et al.. Optimized Convolutional Neural Network for Automatic Detection of COVID-19. CMC-Computers, Materials & Continua, 2022, 70 (1), pp.1159-1175. ⟨10.32604/cmc.2022.017178⟩. ⟨hal-04162700⟩

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