A systematic review on deep structured learning for COVID-19 screening using chest CT from 2020 to 2022

KC Santosh, D GhoshRoy, S Nakarmi - Healthcare, 2023 - mdpi.com
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel
coronavirus. The World Health Organization (WHO) designated it as a global pandemic on …

A comprehensive review of machine learning used to combat COVID-19

R Gomes, C Kamrowski, J Langlois, P Rozario, I Dircks… - Diagnostics, 2022 - mdpi.com
Coronavirus disease (COVID-19) has had a significant impact on global health since the
start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed …

[HTML][HTML] Ensemble deep learning derived from transfer learning for classification of COVID-19 patients on hybrid deep-learning-based lung segmentation: a data …

AK Dubey, GL Chabert, A Carriero, A Pasche… - Diagnostics, 2023 - mdpi.com
Background and motivation: Lung computed tomography (CT) techniques are high-
resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease …

Performance analysis for COVID-19 diagnosis using custom and state-of-the-art deep learning models

AT Nagi, MJ Awan, MA Mohammed, A Mahmoud… - Applied Sciences, 2022 - mdpi.com
The modern scientific world continuously endeavors to battle and devise solutions for newly
arising pandemics. One such pandemic which has turned the world's accustomed routine …

Performance optimization of hunger games search for multi-threshold COVID-19 image segmentation

S Hao, C Huang, AA Heidari, Q Shao… - Multimedia Tools and …, 2024 - Springer
COVID-19 X-ray images are a vital approach for diagnosing whether a patient has an
infection. By using multi-threshold image segmentation (MIS) technology to segment the …

[HTML][HTML] Application of a novel deep learning technique using CT images for COVID-19 diagnosis on embedded systems

H Ulutas, ME Sahin, MO Karakus - Alexandria Engineering Journal, 2023 - Elsevier
Problem A novel coronavirus (COVID-19) has created a worldwide pneumonia epidemic,
and it's important to make a computer-aided way for doctors to use computed tomography …

COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images

L Fang, X Wang - biocybernetics and biomedical engineering, 2022 - Elsevier
Abstract Corona virus disease 2019 (COVID-19) testing relies on traditional screening
methods, which require a lot of manpower and material resources. Recently, to effectively …

Medical images classification using deep learning: a survey

R Kumar, P Kumbharkar, S Vanam… - Multimedia Tools and …, 2024 - Springer
Deep learning has made significant advancements in recent years. The technology is
rapidly evolving and has been used in numerous automated applications with minimal loss …

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold

Y Rao, Q Lv, S Zeng, Y Yi, C Huang, Y Gao… - … signal processing and …, 2023 - Elsevier
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19.
The GGO in computed tomography (CT) images has various features and low-intensity …

COVID-19 infection analysis framework using novel boosted CNNs and radiological images

SH Khan, TJ Alahmadi, T Alsahfi, AA Alsadhan… - Scientific Reports, 2023 - nature.com
COVID-19, a novel pathogen that emerged in late 2019, has the potential to cause
pneumonia with unique variants upon infection. Hence, the development of efficient …