CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans

Automatic lung lesion segmentation of chest computer tomography (CT) scans is considered a pivotal stage toward accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/ups...

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Veröffentlicht in:	IEEE transactions on artificial intelligence Jg. 2; H. 3; S. 283 - 297
Hauptverfasser:	Mahmud, Tanvir, Rahman, Md Awsafur, Fattah, Shaikh Anowarul, Kung, Sun-Yuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2021
Schlagworte:	Artificial intelligence (AI) biomedical imaging Computed tomography computer aided analysis Computer architecture COVID-19 image segmentation Lesions neural networks Optimization Semantics Three-dimensional displays
ISSN:	2691-4581, 2691-4581
Online-Zugang:	Volltext
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