Vision and convolutional transformers for Alzheimer’s disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps
Ibrahem Afifi, Mostafa Elgendy, Mohamed Abdelfatah, Shaker El-Sappagh · 2025
Read Vision and convolutional transformers for Alzheimer’s disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps by Ibrahem Afifi, Mostafa Elgendy, Mohamed Abdelfatah, Shaker El-Sappagh (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12764722.txt.
Description
Ibrahem Afifi and co-authors review AI designs for diagnosing Alzheimer's disease in this 2025 paper. The designs pair vision transformers, a newer kind of image network, with the classic swirling filters of convolutional models. The review gathers 68 studies drawn from 564 papers across five databases.
The authors sort the studies by model shape, data kind, and how data sources are merged. Hybrid designs that blend both network families are on the rise. Gaps stand out: few studies track the slide from mild impairment into full disease, and many cannot be rerun by others. The paper closes with a look at large vision models and lighter, clearer designs for clinic use.
Catalog record
| CREATOR | Ibrahem Afifi, Mostafa Elgendy, Mohamed Abdelfatah, Shaker El-Sappagh |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 803 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |