Mt-ncov-net: a multitask deep-learning framework for efficient diagnosis of covid-19 using tomography scans
W Ding, M Abdel-Basset, H Hawash… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The localization and segmentation of the novel coronavirus disease of 2019 (COVID-19)
lesions from computerized tomography (CT) scans are of great significance for developing …
lesions from computerized tomography (CT) scans are of great significance for developing …
Automated lung-related pneumonia and COVID-19 detection based on novel feature extraction framework and vision transformer approaches using chest X-ray …
According to research, classifiers and detectors are less accurate when images are blurry,
have low contrast, or have other flaws which raise questions about the machine learning …
have low contrast, or have other flaws which raise questions about the machine learning …
Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation
Abstract Background and Objective: Coronavirus disease 2019 (COVID-19) is a highly
contagious virus spreading all around the world. Deep learning has been adopted as an …
contagious virus spreading all around the world. Deep learning has been adopted as an …
MSD-Net: Multi-scale discriminative network for COVID-19 lung infection segmentation on CT
B Zheng, Y Liu, Y Zhu, F Yu, T Jiang, D Yang… - Ieee …, 2020 - ieeexplore.ieee.org
Since the first patient reported in December 2019, 2019 novel coronavirus disease (COVID-
19) has become global pandemic with more than 10 million total confirmed cases and 500 …
19) has become global pandemic with more than 10 million total confirmed cases and 500 …
A novel threshold-based segmentation method for quantification of COVID-19 lung abnormalities
Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the
lives of more than 3.75 million people worldwide. Consequently, methods for accurate …
lives of more than 3.75 million people worldwide. Consequently, methods for accurate …
[HTML][HTML] Framework for COVID-19 segmentation and classification based on deep learning of computed tomography lung images
Abstract Corona Virus Disease 2019 (COVID-19) has affected millions of people worldwide
and caused more than 6.3 million deaths (World Health Organization, June 2022). Increased …
and caused more than 6.3 million deaths (World Health Organization, June 2022). Increased …
A sustainable deep learning-based framework for automated segmentation of COVID-19 infected regions: Using U-Net with an attention mechanism and boundary …
COVID-19 has been spreading rapidly, affecting billions of people globally, with significant
public health impacts. Biomedical imaging, such as computed tomography (CT), has …
public health impacts. Biomedical imaging, such as computed tomography (CT), has …
COVLIAS 2.0-cXAI: Cloud-based explainable deep learning system for COVID-19 lesion localization in computed tomography scans
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation
and the role of explainable artificial intelligence (AI) for understanding lesion localization …
and the role of explainable artificial intelligence (AI) for understanding lesion localization …
A teacher–student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images
Automatic segmentation of infected regions in computed tomography (CT) images is
necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the …
necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the …
GOLF-Net: Global and local association fusion network for COVID-19 lung infection segmentation
X Xu, L Gao, L Yu - Computers in Biology and Medicine, 2023 - Elsevier
The global spread of the Corona Virus Disease 2019 (COVID-19) has caused significant
health hazards, leading researchers to explore new methods for detecting lung infections …
health hazards, leading researchers to explore new methods for detecting lung infections …