| ACC | Accuracy |
| AUC | Area under the receiver operating characteristic curve |
| ACR | American College of Radiology |
| CBAM | Convolutional Block Attention Module |
| CE | Cross-Entropy |
| CI | Confidence Interval |
| CNNs | Convolutional Neural Networks |
| DL | Deep Learning |
| FC | Fully Connected layer |
| FL | Focal Loss |
| FN | False Negative |
| FNA | Fine-Needle Aspiration |
| FP | False Positive |
| FP16 | Half-precision floating-point |
| GAP | Global Average Pooling |
| IoU | Intersection over Union |
| LR | Learning Rate |
| mAP50 | Mean Average Precision at an IoU threshold of 0.5 |
| ML | Machine Learning |
| PACS | Picture Archiving and Communication System |
| ResNet | Residual Network |
| RFs | Random Forests |
| ROI | Region-of-Interest |
| SD | Standard Deviation |
| SEN | Sensitivity |
| SPE | Specificity |
| SVMs | Support Vector Machines |
| TI-RADS | Thyroid Imaging Reporting and Data System |
| TN | True Negative |
| TP | True Positive |
| US | Ultrasound |
| XGBoost | Extreme Gradient Boosting |
| YOLO | You Only Look Once |