Table 1 List of abbreviations and their definitions used in the study.
Abbreviation | Definition |
|---|---|
3D | Three-dimensional |
CT | Computed tomography |
GAN | Generative adversarial network |
PPO | Proximal policy optimization |
TLSTM | Transductive long short-term memory |
LIDC-IDRI | Lung image database consortium image collection |
HD | Hausdorff distance |
ED | Euclidean distance |
WHO | World Health Organization |
X-rays | X-radiation |
MRI | Magnetic resonance imaging |
DRL | Deep reinforcement learning |
U-Net | U-shaped network |
Mask R-CNN | Mask region-based convolutional neural network |
AI | Artificial intelligence |
U-Net++ | U-shaped++ |
TB | Tuberculosis |
LDANet | Lung-dense attention network |
RSA | Residual spatial attention |
GCA | Gated channel attention |
DAGM | Dual attention guidance module |
LDB | Lightweight dense block |
PTB | Positioned transpose block |
CXR | Chest X-ray |
Xception | Extreme inception |
ResNet-18 | Residual network-18 |
COVID-19 | Coronavirus disease 2019 |
FL | Federated learning |
SSSOA | Salp shuffled shepherd optimization algorithm |
VGG16 | Visual geometry group 16 |
CAD | Computer-aided diagnostic |
EfficientNet B3 | Efficient network B3 |
T-Net | T-shaped network |
CenterNet | Center-based object detection network |
NASNet | Neural architecture search network |
TFDM | Differential memory |
WOA | Whale optimization algorithm |
SVM | Support vector machine |
CapsNet | Capsule neural network |
WSTSA | Wormhole and Salp swarm strategy enhanced tree-seed algorithm |
HM-LeNet | Hybrid mobile LeNet |
SNP | Single nucleotide polymorphism |
LDN | Lightweight deep network |
ANN | Artificial neural network |
ROI | Region of interest |
2D | Two-dimensional |
GGO | Ground glass opacity |
CBAM | Convolutional block attention module |
ASPP | Atrous spatial pyramid pooling |
ReLU | Rectified linear unit |
DSC | Dice similarity coefficient |
FSIM | Feature similarity index measure |
APSO | Adaptive particle swarm optimization |
XGBoost | Extreme gradient boosting |
HRCT | High-resolution computed tomography |
IoT | Internet of things |
OFCMNN | Optimized fuzzy C-means neural network |
KM-DTCL | Kernel multilayer deep transfer convolutional learning |
Coarse Seg-net | Coarse segmentation subnetwork |
Fine Seg-net | Fine segmentation subnetwork |
Class-net | Classification subnetwork |
ROC | Receiver operating characteristic |
AUC | Area under the curve |
HRNet | High-resolution network |
DLN | Deep learning nomogram |
ITF | Intrathoracic fat |
IPN | Intranodular and perinodular regions |
LASSO | Least absolute shrinkage and selection operator |
PET | positron emission tomography |
KAN | Kolmogorov–Arnold networks |
SE | Squeeze-and-excitation |
ViT | Vision transformer |
Grad-CAM | Gradient-weighted class activation mapping |
YOLOv8 | You Only Look Once version 8 |
DCGAN | Deep convolutional generative adversarial network |
FPN | Feature pyramid network |
MSDA | Multi-scale dilation attention |
GCSAM | Global channel spatial attention mechanism |
CNDNet | Candidate nodule detection network |
FPRNet | False positive reduction network |
HPFF | Hierarchical progressive feature fusion |
LUNA16 | Lung nodule analysis 2016 |
GK | Gustafson and Kessel |
TRPO | Trust region policy optimization |
RNN | Recurrent neural network |
MDP | Markov decision process |
KL | Kullback-Leibler |
EMD | Earth Mover’s distance |
BN | Batch normalization |
FNIH | Foundation for the national institutes of health |
FDA | Food and drug administration |
XML | Extensible markup language |
IoU | Intersection over union |
RPN | Region proposal network |
ADAM | Adaptive moment estimation |
GPU | Graphics processing unit |
GB | Gigabyte |
FGSM | Fast gradient sign method |
