[HTML][HTML] Research on artificial-intelligence-assisted medicine: a survey on medical artificial intelligence
F Gou, J Liu, C Xiao, J Wu - Diagnostics, 2024 - mdpi.com
With the improvement of economic conditions and the increase in living standards, people's
attention in regard to health is also continuously increasing. They are beginning to place …
attention in regard to health is also continuously increasing. They are beginning to place …
Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey
MAA Al-qaness, J Zhu, D AL-Alimi, A Dahou… - … Methods in Engineering, 2024 - Springer
In medical imaging, the last decade has witnessed a remarkable increase in the availability
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …
A cascaded multi-stage framework for automatic detection and segmentation of pulmonary nodules in developing countries
Z Zhou, F Gou, Y Tan, J Wu - IEEE Journal of Biomedical and …, 2022 - ieeexplore.ieee.org
Lung cancer has the highest mortality rate among all malignancies. Non-micro pulmonary
nodules are the primary manifestation of early-stage lung cancer. If patients can be detected …
nodules are the primary manifestation of early-stage lung cancer. If patients can be detected …
CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation
Lung nodule segmentation plays a crucial role in early-stage lung cancer diagnosis, and
early detection of lung cancer can improve the survival rate of the patients. The approaches …
early detection of lung cancer can improve the survival rate of the patients. The approaches …
Volumetric lung nodule segmentation using adaptive roi with multi-view residual learning
Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung
cancer, enhancing patient survival possibilities. A number of nodule segmentation …
cancer, enhancing patient survival possibilities. A number of nodule segmentation …
Deep deconvolutional residual network based automatic lung nodule segmentation
Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer
analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However …
analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However …
Lung tumor image segmentation from computer tomography images using MobileNetV2 and transfer learning
Background: Lung cancer is one of the most fatal cancers worldwide, and malignant tumors
are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms …
are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms …
Segmentation of lung nodules on CT images using a nested three-dimensional fully connected convolutional network
In computer-aided diagnosis systems for lung cancer, segmentation of lung nodules is
important for analyzing image features of lung nodules on computed tomography (CT) …
important for analyzing image features of lung nodules on computed tomography (CT) …
Conventional filtering versus u-net based models for pulmonary nodule segmentation in ct images
Lung cancer is considered one of the deadliest diseases in the world. An early and accurate
diagnosis aims to promote the detection and characterization of pulmonary nodules, which …
diagnosis aims to promote the detection and characterization of pulmonary nodules, which …
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 …