Selection of proper artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS
Geng Lu, Yuheng Wang, Chunying Li, Cong Wu · 2026
Read Selection of proper artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS by Geng Lu, Yuheng Wang, Chunying Li, Cong Wu (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/PMC13556066.txt.
Description
Geng Lu and three colleagues set out in 2026 to rank artificial intelligence methods built for reading liver cancer CT scans. Hundreds of competing techniques exist, and clinics need a fair way to pick among them. The team searched five databases, sorted the methods by task, then used expert rounds to agree on selection criteria before applying fuzzy decision methods that weigh each option against an ideal.
They catalogued 260 techniques and settled on seven criteria, including validity, accuracy, speed, and cost. A deep learning reconstruction model topped the detection and diagnosis group, and a combined deep learning, radiomics, and clinical model topped the treatment group. Segmentation and prediction tasks each had their own winner. The rankings give radiology teams a structured shortlist instead of a marketing contest.
Catalog record
| CREATOR | Geng Lu, Yuheng Wang, Chunying Li, Cong Wu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 72179 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |