Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN‐Transformer Hybrids in Multimodal Imaging
Solomon Buabeng Antwi, Peter Appiahene, Ben Beklisi Kwame Ayawli, Peter Nimbe, Vincenzo Positano · 2026
Read Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN‐Transformer Hybrids in Multimodal Imaging by Solomon Buabeng Antwi, Peter Appiahene, Ben Beklisi Kwame Ayawli, Peter Nimbe, Vincenzo Positano (2026) 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/PMC13270495.txt.
Description
Solomon Buabeng Antwi led a 2026 systematic review and meta-analysis, a study that pools past studies, on deep learning for brain tumour scans. The review looked at hybrid models that join convolutional networks, which read local image patterns, with transformers, which read wider context across an image.
The authors searched four databases for work from January 2021 to May 2025 and kept 94 studies from 1,876 first found. Across those studies, pooled diagnostic accuracy, meaning how often the models were right, was 93.5 percent. The review also weighed computing cost, methods that make extra training images, and the joining of several scan types.
Catalog record
| CREATOR | Solomon Buabeng Antwi, Peter Appiahene, Ben Beklisi Kwame Ayawli, Peter Nimbe, Vincenzo Positano |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 84651 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |