SynthXNet: Adversarial Learning for Data Augmentation in CoVID Severity Classification from X-Rays-Reference-Cited by-同舟云学术

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SynthXNet: Adversarial Learning for Data Augmentation in CoVID Severity Classification from X-Rays

Published:2023-10-19 Issue: Volume: Page:1-8
ISSN:
Container-title:2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)
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Author:

Thombre Supriya S.¹,Malik Latesh²,Kumar Sanjay³

Affiliation:

1. Kalinga University,Raipur,Chhattisgarh,India

2. Government College of Engineering,Department of Computer Science and Engineering,Nagpur,Maharashtra,India

3. Kalinga University,Department of Computer Science and Engineering,Raipur,Chhattisgarh,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10370005/10370765/10370831.pdf?arnumber=10370831

Reference18 articles.

1. COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

2. Structural Attention Graph Neural Network for Diagnosis and Prediction of COVID-19 Severity

3. COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images

4. Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels

5. Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation

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