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| Author name (year) | Dataset details | Methods and approaches | Summary |
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| Li et al. [65] (2020) | ADNI-1 including MRI as well as PET brain scans | LDF-based modelling predicated on the combination of low-ranking and discriminant correlations representation through fusion of multimodal datasets | The researcher experimented for the adequate extraction of latent characteristics for submodal data using the low-level representation approach which has helped in removing noise information and an enhanced system performance |
| El-Sappagh et al. [66] (2020) | MRI brain scans | A multitasking deep learning framework based on the five series data paradigms towards Alzheimer progression and detection on fused CNN-BiLSTM | The authors demonstrated a model that predicts AD progression as a multiclass classification task and four critical cognitive scores as regression tasks. The experimental results have shown the model to be medically advanced |
| Suárez-Araujo et al. [67] (2021) | ADNI dataset consisting with sample of 128 MCI patients and 203 controls. | A hybrid ANN-based AD classification system | The ANN method suggested achieves good diagnosis accuracy, even if it is just based on conventional medical trials. These findings indicate that method is particularly appropriate for primary treatment, helping doctors to work with suspicion of cognitive impairment |
| Pei et al. [68] (2021) | ADNI | Pseudo-3D block and an enlarged global context block, incorporated in a cascaded approach utilizing long-range dependencies into the a remnant backbone block | Experimental findings done by the authors show 89.27% of the AD/NC accuracy and 87.57% of the light cognitive impairments/NC with CNN modelling while 0.5% more accurate than the backbone is reported |
| Tomassini et al. [69] (2021) | ADNI and OASIS | 3D-ConvLSTM-based early AD diagnosis mechanism utilizing full-resolution brain images | The researcher proposed a framework which works effectively to distinguish between CN and AD patients with a classification accuracy of 86% conducted using a modular GPU cloud service |
| Proposed approach | ADNI | Bayesian optimization with binary as well as ternary classification on augmented 3D-MRI data scans employing long short-term memory (LSTM) | The method used fewer iterations, and the model training was improved alongside obtaining the highest validation score on both ternary as well as binary classification |
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