Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks
Deep learning techniques have been extensively used in computerized pulmonary nodule
analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in …
analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in …
The diagnosis performance of convolutional neural network in the detection of pulmonary nodules: a systematic review and meta-analysis
X Zhang, B Liu, K Liu, L Wang - Acta Radiologica, 2023 - journals.sagepub.com
Background Pulmonary nodules are an early imaging indication of lung cancer, and early
detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the …
detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the …
Ensemble learning framework with GLCM texture extraction for early detection of lung cancer on CT images
Lung cancer has emerged as a major cause of death among all demographics worldwide,
largely caused by a proliferation of smoking habits. However, early detection and diagnosis …
largely caused by a proliferation of smoking habits. However, early detection and diagnosis …
Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images
F Heutink, V Koch, B Verbist, WJ van der Woude… - Computer methods and …, 2020 - Elsevier
Background and objective Performing patient-specific, pre-operative cochlea CT-based
measurements could be helpful to positively affect the outcome of cochlear surgery in terms …
measurements could be helpful to positively affect the outcome of cochlear surgery in terms …
Deep CNN models for pulmonary nodule classification: model modification, model integration, and transfer learning
BACKGROUND: Deep learning has made spectacular achievements in analysing natural
images, but it faces challenges for medical applications partly due to inadequate images …
images, but it faces challenges for medical applications partly due to inadequate images …
Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image
Purpose: Segmentation of lung nodules in chest CT images is essential for image-driven
lung cancer diagnosis and follow-up treatment planning. Manual segmentation of lung …
lung cancer diagnosis and follow-up treatment planning. Manual segmentation of lung …
[HTML][HTML] A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques
J Wu, T Qian - Journal of Medical Artificial Intelligence, 2019 - jmai.amegroups.org
Lung cancer is the top cause for deaths by cancers whose 5-year survival rate is less than
20%. To improve the survival rate of patients with lung cancers, the early detection and early …
20%. To improve the survival rate of patients with lung cancers, the early detection and early …
Multi-branch ensemble learning architecture based on 3D CNN for false positive reduction in lung nodule detection
H Cao, H Liu, E Song, G Ma, X Xu, R **, T Liu… - IEEE …, 2019 - ieeexplore.ieee.org
It is critical to have accurate detection of lung nodules in CT images for the early diagnosis of
lung cancer. In order to achieve this, it is necessary to reduce the false positive rate of …
lung cancer. In order to achieve this, it is necessary to reduce the false positive rate of …
Synthetic CT images for semi-sequential detection and segmentation of lung nodules
Accurately detecting and segmenting lung nodules from CT images play a critical role in the
earlier diagnosis of lung cancer and thus have attracted much interest from the research …
earlier diagnosis of lung cancer and thus have attracted much interest from the research …
Atrous convolution for binary semantic segmentation of lung nodule
Accurately estimating the size of tumours and reproducing their boundaries from lung CT
images provides crucial information for early diagnosis, staging and evaluating patients …
images provides crucial information for early diagnosis, staging and evaluating patients …