Deep-Covid-SEV: an Ensemble 2D and 3D CNN-Based Approach for Covid-19 Severity Prediction from 3D CT-SCANS-Reference-Cited by-同舟云学术

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Deep-Covid-SEV: an Ensemble 2D and 3D CNN-Based Approach for Covid-19 Severity Prediction from 3D CT-SCANS

Published:2023-06-04 Issue: Volume: Page:
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Container-title:2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)
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Author:

Bougourzi Fares¹,Dornaika Fadi²,Nakib Amir¹,Distante Cosimo³,Taleb-Ahmed Abdelmalik⁴

Affiliation:

1. University Paris-Est Creteil,Laboratoire LISSI,Vitry sur Seine, Paris,France,94400

2. Basque Foundation for Science,UPV/EHU, IKERBASQUE,Bilbao,Spain

3. National Research Council of Italy,Lecce,Italy,73100

4. Université Polytechnique Hauts de France, UPHF,IEMN UMR CNRS 8520,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10192576/10192577/10192927.pdf?arnumber=10192927

Reference22 articles.

1. Aggregated Residual Transformations for Deep Neural Networks

2. Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans

3. ILC-Unet++ for Covid-19 Infection Segmentation

4. Attention U-Net: Learning Where to Look for the Pancreas;oktay;arXiv 1804 03999,2018

5. Deep transparent prediction through latent representation analysis;kollias;arXiv preprint arXiv 2009 07151,2020

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