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CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes

Autor: Aksh Garg, Shray Alag, Dominique Duncan

Publikováno v: Diagnostics, Vol 14, Iss 3, p 337 (2024)

Given the pronounced impact COVID-19 continues to have on society—infecting 700 million reported individuals and causing 6.96 million deaths—many deep learning works have recently focused on the virus’s diagnosis. However, assessing severity ha

Externí odkaz: https://doaj.org/article/1226fe38e84646cc851a827b928c1a60

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Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

Autor: Aksh Garg, Sana Salehi, Marianna La Rocca, Rachael Garner, Dominique Duncan

Publikováno v: Expert Systems with Applications

With COVID-19 cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e3a16f36f090ce0358c8f2d6bbb9b16
https://pubmed.ncbi.nlm.nih.gov/35075334

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