Table 1. The summary of the studies.
| Reference | Year | Dataset | Models | Classes | Accuracy % |
|---|---|---|---|---|---|
| Shanmugam et al. (2022) | 2022 | ADNI | AlexNet | CN | 97.34% |
| EMCI | 97.51% | ||||
| LMCI | 95.19% | ||||
| MCI | 96.82% | ||||
| AD | 94.08% | ||||
| ResNet-18 | CN | 98.88% | |||
| EMCI | 99.14% | ||||
| LMCI | 98.88% | ||||
| MCI | 98.71% | ||||
| AD | 97.51% | ||||
| GoogleNet | CN | 97.17% | |||
| EMCI | 98.28% | ||||
| LMCI | 97.60% | ||||
| MCI | 98.37 | ||||
| AD | 96.39% | ||||
| Mehmood et al. (2021) | 2021 | ADNI | CNN | CN vs AD (Group A) | 95.38% |
| CN vs AD (Group B) | 98.73% | ||||
| Mohammadjafari et al. (2021) | 2021 | ADNI-1 | VGG-16 | AD, CN | 88.50% |
| ResNet50 | 83.88% | ||||
| DenseNet121 | 94.75% | ||||
| Sethi et al. (2022) | 2022 | ADNI | CNN | CN vs AD | 82.32% |
| CNN+SVM | 89.40% | ||||
| Naz, Ashraf & Zaib (2021) | 2021 | ADNI | VGG-19 | MCI vs AD | 99.27% |
| VGG-16 | CN vs AD | 98.89% | |||
| AlexNet | CN vs AD | 91.38% | |||
| VGG-16 | MCI vs CN | 97.06% | |||
| Farooq et al. (2017) | 2017 | ADNI | AlexNet | AD, LMCI, MCI, CN | 98.88% |
| ResNet-18 | 98.01% | ||||
| ResNet-152 | 98.14% | ||||
| Savaş (2022) | 2022 | ADNI | EfficientNetB0 | CN, MCI, AD | 92.98% |
| EfficientNetB1 | 91.91% | ||||
| Li, Cheng & Liu (2017) | 2017 | ADNI | CNN_S3 | CN, AD | 84.12% |
| CAE_S2 | 82.24% | ||||
| CAE_S3 | 81.19% | ||||
| CAE_S4 | 76.17% | ||||
| Hybrid | 88.31% | ||||
| Khan et al. (2022) | 2022 | ADNI | XGB + DT + SVM | CN, MCI, AD | 95.75% |
| Mohi ud din dar et al. (2023) | 2023 | ADNI | CNN | CN, LMCI, EMCI, MCI, AD | 96.22% |
| Mora-Rubio et al. (2023) | 2023 | ADNI, OASIS | DenseNet | CN vs MCI | 66.41% |
| EfficientNet | |||||
| CN vs AD | 89.02% | ||||
| VIT | CN vs LMCI | 80.56% | |||
| Siamese | CN vs EMCI | 67.19% |