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

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

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1016/j.compeleceng.2022.108479

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Convolutional Neural Network

Artificial Intelligence

Infectious Diseases (Medical Specialty)

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Convolutional neural network Artificial intelligence Coronavirus disease 2019 (COVID-19) Computer science Computed tomography Pattern recognition (psychology) Deep learning Categorization Cross entropy Function (biology) Convergence (economics) Machine learning Medicine Radiology Disease Infectious disease (medical specialty) Pathology Economics Economic growth Biology Evolutionary biology

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.compeleceng.2022.108479
OA Status: green
Cited By: 31
References: 34
Related Works: 10
OpenAlex ID: https://openalex.org/W4309015005

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DOI: https://doi.org/10.1016/j.compeleceng.2022.108479

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

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-11-14

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

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Landing page: https://doi.org/10.1016/j.compeleceng.2022.108479

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OA URL: https://www.ncbi.nlm.nih.gov/pmc/articles/9659516

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Concepts: Convolutional neural network, Artificial intelligence, Coronavirus disease 2019 (COVID-19), Computer science, Computed tomography, Pattern recognition (psychology), Deep learning, Categorization, Cross entropy, Function (biology), Convergence (economics), Machine learning, Medicine, Radiology, Disease, Infectious disease (medical specialty), Pathology, Economics, Economic growth, Biology, Evolutionary biology

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Cited by: 31

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Citations by year (recent): 2025: 6, 2024: 13, 2023: 12

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Related works (count): 10

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