WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images

• Autorzy: Murugan R. , Goel Tripti , Mirjalili Seyedali , Chakrabartty Deba Kumar

Identyfikatory

DOI

10.1016/j.bbe.2021.10.004

• Języki publikacji: EN

Abstrakty

EN

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography (CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists’ burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.

Słowa kluczowe

EN

early diagnosis; COVID-19 diagnosis; whale optimization algorithm; machine learning; deep learning

PL

wczesna diagnoza; diagnoza COVID-19; algorytm optymalizacji wielorybów; uczenie maszynowe; uczenie głębokie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 4
• Strony: 1702--1718
• Opis fizyczny: Bibliogr. 72 poz., rys., tab., wykr.

Twórcy

autor

Murugan R.

• Bio-Medical Imaging Laboratory(BIOMIL), Department of Electronics and communication Engineering, National Institute of Technology Silchar, Assam 788010, India

autor

Goel Tripti

• Bio-Medical Imaging Laboratory(BIOMIL), Department of Electronics and communication Engineering, National Institute of Technology Silchar, Assam, India

autor

Mirjalili Seyedali

• Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea

autor

Chakrabartty Deba Kumar

• Department of Radiology, Silchar Medical College and Hospital, Silchar, Assam, India

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