ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

Alex Noel Joseph Raj; Haipeng Zhu; Asiya Khan; Zhemin Zhuang; Zengbiao Yang; G. V.Vijayalakshmi Mahesh; Ganesan Karthik

doi:10.7717/peerj-cs.349

ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

Alex Noel Joseph Raj
, Haipeng Zhu
, Asiya Khan
, Zhemin Zhuang^*
, Zengbiao Yang
, G. V.Vijayalakshmi Mahesh
, Ganesan Karthik

^*Corresponding author for this work

Shantou University
BMS Institute of Technology and Management
Madras Medical College

Research output: Contribution to journal › Article › peer-review

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Original language	English
Number of pages	0
Journal	PeerJ Computer Science
Volume	7
Issue number	0
Early online date	26 Jan 2021
DOIs	https://doi.org/10.7717/peerj-cs.349
Publication status	Published - 26 Jan 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.7717/peerj-cs.349

Cs-52665-ADID UNE Revised CleanFinal published version, 20.9 MB

Cite this

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title = "ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans",

author = "Raj, \{Alex Noel Joseph\} and Haipeng Zhu and Asiya Khan and Zhemin Zhuang and Zengbiao Yang and Mahesh, \{G. V.Vijayalakshmi\} and Ganesan Karthik",

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AU - Raj, Alex Noel Joseph

AU - Zhu, Haipeng

AU - Khan, Asiya

AU - Zhuang, Zhemin

AU - Yang, Zengbiao

AU - Mahesh, G. V.Vijayalakshmi

AU - Karthik, Ganesan

PY - 2021/1/26

Y1 - 2021/1/26

UR - https://pearl.plymouth.ac.uk/secam-research/338/

U2 - 10.7717/peerj-cs.349

DO - 10.7717/peerj-cs.349

M3 - Article

SN - 2376-5992

VL - 7

JO - PeerJ Computer Science

JF - PeerJ Computer Science

IS - 0

ER -

ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

UN SDGs

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