UCFilTransNet: Cross-Filtering Transformer-based network for CT image segmentation
L Li, Q Liu, X Shi, Y Wei, H Li, H Xiao - Expert Systems with Applications, 2024 - Elsevier
U-Net is a common segmentation model and achieves good segmentation results, but U-Net
has a large semantic gap between the encoder and decoder. In addition, both high …
has a large semantic gap between the encoder and decoder. In addition, both high …
Covid-19 detection from chest x-rays using trained output based transfer learning approach
The recent Coronavirus disease (COVID-19), which started in 2019, has spread across the
globe and become a global pandemic. The efficient and effective COVID-19 detection using …
globe and become a global pandemic. The efficient and effective COVID-19 detection using …
A COVID-19 medical image classification algorithm based on Transformer
K Ren, G Hong, X Chen, Z Wang - Scientific Reports, 2023 - nature.com
Abstract Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread
rapidly throughout the world. This paper proposes a novel deep learning network based on …
rapidly throughout the world. This paper proposes a novel deep learning network based on …
SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images
Automatic segmentation of lung opacification from computed tomography (CT) images
shows excellent potential for quickly and accurately quantifying the infection of Coronavirus …
shows excellent potential for quickly and accurately quantifying the infection of Coronavirus …
Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images
Due to the irregular shapes, various sizes and indistinguishable boundaries between the
normal and infected tissues, it is still a challenging task to accurately segment the infected …
normal and infected tissues, it is still a challenging task to accurately segment the infected …
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 …
Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis
Abstract Coronavirus (COVID-19) and its new strain resulted in massive damage to society
and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and …
and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and …
A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis
MF Aslan - Chemometrics and Intelligent Laboratory Systems, 2022 - Elsevier
This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep
learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the …
learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the …
Comprehensive survey of machine learning systems for COVID-19 detection
The last two years are considered the most crucial and critical period of the COVID-19
pandemic affecting most life aspects worldwide. This virus spreads quickly within a short …
pandemic affecting most life aspects worldwide. This virus spreads quickly within a short …
[HTML][HTML] Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients
F Gholamiankhah, S Mostafapour… - Iranian Journal of …, 2022 - ncbi.nlm.nih.gov
Background: Automated image segmentation is an essential step in quantitative image
analysis. This study assesses the performance of a deep learning-based model for lung …
analysis. This study assesses the performance of a deep learning-based model for lung …