Enhanced framework for COVID-19 prediction with computed tomography scan images using dense convolutional neural network and novel loss function.

Motwani, Anand; Shukla, Piyush Kumar; Pawar, Mahesh; Kumar, Manoj; Ghosh, Uttam; Alnumay, Waleed; Nayak, Soumya Ranjan

Motwani, Anand; Shukla, Piyush Kumar; Pawar, Mahesh; Kumar, Manoj; Ghosh, Uttam; Alnumay, Waleed; Nayak, Soumya Ranjan.

Afiliação

Motwani A; Faculty, School of Computing Science & Engineering, VIT Bhopal University, Sehore (MP), 466114, India.
Shukla PK; Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal (MP), 462033, India.
Pawar M; Department of Information Technology, University Institute of Technology, RGPV, Bhopal (MP), 462033, India.
Kumar M; Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, UAE.
Ghosh U; Department of Computer Science & Data Science Meharry School of Computational Sciences Nashville, TN, USA.
Alnumay W; Riyadh Community College, CS department, King Saud University, Riyadh, SA.
Nayak SR; School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India.

Comput Electr Eng ; 105: 108479, 2023 Jan.

Article em En | MEDLINE | ID: mdl-36406625

Palavras-chave

COVID-19; Chest CT-images; Classification; Deep learning; Dense-convolutional neural network; Loss function; Optimization; Prediction; SARS-CoV-2

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Electr Eng Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Índia

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