A robust stacked ensemble strategy with multi-optimizer CNN models for skin cancer classification
Arvind Panwar, Jyoti Agarwal, Shruti Vashist, Yogita Arora, Achin Jain, Ondrej Krejcar, Robert Frischer, Hamidreza Namazi · 2026
Read A robust stacked ensemble strategy with multi-optimizer CNN models for skin cancer classification by Arvind Panwar, Jyoti Agarwal, Shruti Vashist, Yogita Arora, Achin Jain, Ondrej Krejcar, Robert Frischer, Hamidreza Namazi (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/pmc-13376220.txt.
Description
Arvind Panwar and co-authors built in 2026 a combined model for sorting skin lesions into cancer types from images. Early finding of skin cancer greatly raises survival, and computer aids are gaining ground. The team trained four deep networks, each with a different training rule, on a melanoma image set.
More than ninety blends were tested, using voting schemes and stacked blends where a second model learns from the first models outputs. The best stack reached high test accuracy and a strong score for telling types apart. Blending, above all stacking, beat single models in their tests. The authors present the stack as a more reliable aid for melanoma finding.
Catalog record
| CREATOR | Arvind Panwar, Jyoti Agarwal, Shruti Vashist, Yogita Arora, Achin Jain, Ondrej Krejcar, Robert Frischer, Hamidreza Namazi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |