Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
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 …
On the performance of lung nodule detection, segmentation and classification
Computed tomography (CT) screening is an effective way for early detection of lung cancer
in order to improve the survival rate of such a deadly disease. For more than two decades …
in order to improve the survival rate of such a deadly disease. For more than two decades …
Artificial intelligence solution to classify pulmonary nodules on CT
Purpose The purpose of this study was to create an algorithm to detect and classify
pulmonary nodules in two categories based on their volume greater than 100 mm 3 or not …
pulmonary nodules in two categories based on their volume greater than 100 mm 3 or not …
Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume
AL Aswathy, VC SS - Scientific Reports, 2022 - nature.com
Abstract World Health Organization (WHO) declared COVID-19 (COronaVIrus Disease
2019) as pandemic on March 11, 2020. Ever since then, the virus is undergoing different …
2019) as pandemic on March 11, 2020. Ever since then, the virus is undergoing different …
LDNNET: towards robust classification of lung nodule and cancer using lung dense neural network
Y Chen, Y Wang, F Hu, L Feng, T Zhou, C Zheng - IEEE Access, 2021 - ieeexplore.ieee.org
Lung nodule classification plays an important role in diagnosis of lung cancer which is
essential to patients' survival. However, because the number of lung CT images in current …
essential to patients' survival. However, because the number of lung CT images in current …
A novel multi-scale CNNs for false positive reduction in pulmonary nodule detection
D Zhao, Y Liu, H Yin, Z Wang - Expert Systems with Applications, 2022 - Elsevier
Accurate false positive reduction is a significant stage in pulmonary nodule detection. The
existing false positive reduction based methods mainly 3D CNNs due to its high sensitivity of …
existing false positive reduction based methods mainly 3D CNNs due to its high sensitivity of …
Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning
Objectives To use convolutional neural network for fully automated segmentation and
radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI. Methods MR …
radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI. Methods MR …
[PDF][PDF] A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans
We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest
X-ray and Computed Tomography (CT) scans, and the classification and segmentation of …
X-ray and Computed Tomography (CT) scans, and the classification and segmentation of …
Machine learning techniques for pulmonary nodule computer-aided diagnosis using CT images: A systematic review
H Jin, C Yu, Z Gong, R Zheng, Y Zhao, Q Fu - … Signal Processing and …, 2023 - Elsevier
Objective Early detection of pulmonary nodules is critical for the prevention and treatment of
lung cancer. Concomitant with recent advancements in computer performance and …
lung cancer. Concomitant with recent advancements in computer performance and …