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A systematic review of automated segmentation methods and public datasets for the lung and its lobes and findings on computed tomography images
Objectives: Automated computational segmentation of the lung and its lobes and findings in
X-Ray based computed tomography (CT) images is a challenging problem with important …
X-Ray based computed tomography (CT) images is a challenging problem with important …
[HTML][HTML] COVLIAS 1.0: lung segmentation in COVID-19 computed tomography scans using hybrid deep learning artificial intelligence models
Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is
important for the diagnosis of lung severity. The process of automated lung segmentation is …
important for the diagnosis of lung severity. The process of automated lung segmentation is …
[HTML][HTML] COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans
Background: COVID-19 is a disease with multiple variants, and is quickly spreading
throughout the world. It is crucial to identify patients who are suspected of having COVID-19 …
throughout the world. It is crucial to identify patients who are suspected of having COVID-19 …
Inter-variability study of COVLIAS 1.0: hybrid deep learning models for COVID-19 lung segmentation in computed tomography
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography
(CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) …
(CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) …
COVLIAS 1.0 vs. MedSeg: artificial intelligence-based comparative study for automated COVID-19 computed tomography lung segmentation in Italian and Croatian …
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for
COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were …
COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were …
Learning COVID-19 pneumonia lesion segmentation from imperfect annotations via divergence-aware selective training
Automatic segmentation of COVID-19 pneumonia lesions is critical for quantitative
measurement for diagnosis and treatment management. For this task, deep learning is the …
measurement for diagnosis and treatment management. For this task, deep learning is the …
Estimating lung volume capacity from x-ray images using deep learning
S Ghimire, S Subedi - Quantum Beam Science, 2024 - mdpi.com
Estimating lung volume capacity is crucial in clinical medicine, especially in disease
diagnostics. However, the existing estimation methods are complex and expensive, which …
diagnostics. However, the existing estimation methods are complex and expensive, which …
Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis
Background Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification
of abnormalities and may facilitate the diagnosis and assessment of the prognosis of …
of abnormalities and may facilitate the diagnosis and assessment of the prognosis of …
A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images
Recently, the Covid-19 pandemic has affected several lives of people globally, and there is
a need for a massive number of screening tests to diagnose the existence of coronavirus …
a need for a massive number of screening tests to diagnose the existence of coronavirus …
An automated approach for fibroblast cell confluency characterisation and sample handling using aiot for bio-research and bio-manufacturing
Current methods used in cell culture monitoring, characterisation and handling are manual,
time consuming and highly dependent on subjective observations made by human …
time consuming and highly dependent on subjective observations made by human …