| AD | Alzheimer’s Disease |
| ADGNET | Alzheimer’s Disease Grade Network |
| WSL | Weakly Supervised Learning |
| DL | Deep Learning |
| WHO | World Health Organization |
| MCI | Mild Cognitive Impairment |
| CT | Computed Tomography |
| PET | Positron Emission Tomography |
| MRI | Magnetic Resonance Imaging |
| CAD | Computer-Aided Diagnosis |
| CNN | Convolution Neural Network |
| SOTA | State-of-the-Art |
| AM | Attention Module |
| MTL | Multi-Task Learning |
| SIMO | Single-Input-Multi-Output |
| CSN | Classification Sub-Network |
| RSN | Reconstruction Sub-Network |
| GAP | Global Average Pooling |
| FC | Fully Connected |
| CAF | Channel Attention Factors |
| EWMO | Element-Wise Multiplication Operation |
| BN | Batch Norm |
| KACD | Kaggle Alzheimer’s Classification Dataset |
| ROAD | Recognition of Alzheimer’s Disease Dataset |
| TTSF | Train-Test-Split Function |
| TVS | Training-Val Set |
| TS | Testing Set |
| Sen | Sensitivity |
| Spe | Specificity |
| Pr | Precision |
| Acc | Accuracy |