[HTML][HTML] Multiside graph neural network-based attention for local co-occurrence features fusion in lung nodule classification
Early diagnosis of lung cancer is critical as it can save people's lives. Long-range
dependencies within volumetric medical images are essential attributes for accurate lung …
dependencies within volumetric medical images are essential attributes for accurate lung …
A volumetric multi-head attention strategy for lung nodule classification in CT
Pulmonary nodules are the principal lung cancer indicator, whose malignancy is mainly
related to their size, morphological and textural features. Computational deep …
related to their size, morphological and textural features. Computational deep …
Deep fusion of gray level co-occurrence matrices for lung nodule classification
A Saihood, H Karshenas, ARN Nilchi - Plos one, 2022 - journals.plos.org
Lung cancer is a serious threat to human health, with millions dying because of its late
diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for …
diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for …
Multi-Orientation local texture features for guided attention-based fusion in lung nodule classification
Computerized tomography (CT) scan images are widely used in automatic lung cancer
detection and classification. The lung nodules' texture distribution throughout the CT scan …
detection and classification. The lung nodules' texture distribution throughout the CT scan …
Contextual convolutional neural networks for lung nodule classification using Gaussian-weighted average image patches
Lung cancer is the most common cause of cancer-related death. To diagnose lung cancers
in early stages, numerous studies and approaches have been developed for cancer …
in early stages, numerous studies and approaches have been developed for cancer …
Deep feature selection and decision level fusion for lungs nodule classification
The existence of pulmonary nodules exhibits the presence of lung cancer. The Computer-
Aided Diagnostic (CAD) and classification of such nodules in CT images lead to improve the …
Aided Diagnostic (CAD) and classification of such nodules in CT images lead to improve the …
Deep 3D multi-scale dual path network for automatic lung nodule classification
S Wang, X Kuang, Y Zhu, W Zhang… - … Journal of Biomedical …, 2022 - inderscienceonline.com
Lung cancer is the cancer with the highest mortality rate in the USA. Computed tomography
(CT) scans for early diagnosis of pulmonary nodules can detect lung cancer in time. To …
(CT) scans for early diagnosis of pulmonary nodules can detect lung cancer in time. To …
Multi-scale pulmonary nodule classification with deep feature fusion via residual network
The early stage detection of benign and malignant pulmonary nodules plays an important
role in clinical diagnosis. The malignancy risk assessment is usually used to guide the …
role in clinical diagnosis. The malignancy risk assessment is usually used to guide the …
Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning
L Sun, M Zhang, Y Lu, W Zhu, Y Yi, F Yan - Computers in Biology and …, 2024 - Elsevier
The latest developments in deep learning have demonstrated the importance of CT medical
imaging for the classification of pulmonary nodules. However, challenges remain in fully …
imaging for the classification of pulmonary nodules. However, challenges remain in fully …
Adaptive aggregated attention network for pulmonary nodule classification
K Xia, J Chi, Y Gao, Y Jiang, C Wu - Applied Sciences, 2021 - mdpi.com
Lung cancer has one of the highest cancer mortality rates in the world and threatens
people's health. Timely and accurate diagnosis can greatly reduce the number of deaths …
people's health. Timely and accurate diagnosis can greatly reduce the number of deaths …