Q-AVOA-net: A multi-domain, quantum-inspired feature-selection and deep-fuzzy framework for interpretable white blood cell classification

Omid Eslamifar, Mohammadreza Soltani, Seyed Mohammad Jalal Rastegar Fatemi

Open source

DOI
10.1016/j.isci.2026.116507
Published
2026-09
Container
iScience
Publisher
Elsevier BV
Open access
unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1016/j.isci.2026.116507,
  title = {Q-AVOA-net: A multi-domain, quantum-inspired feature-selection and deep-fuzzy framework for interpretable white blood cell classification},
  author = {Omid Eslamifar and Mohammadreza Soltani and Seyed Mohammad Jalal Rastegar Fatemi},
  year = {2026},
  journal = {iScience},
  doi = {10.1016/j.isci.2026.116507},
  url = {https://doi.org/10.1016/j.isci.2026.116507}
}

RIS

TY  - JOUR
TI  - Q-AVOA-net: A multi-domain, quantum-inspired feature-selection and deep-fuzzy framework for interpretable white blood cell classification
AU  - Omid Eslamifar
AU  - Mohammadreza Soltani
AU  - Seyed Mohammad Jalal Rastegar Fatemi
PY  - 2026
JO  - iScience
DO  - 10.1016/j.isci.2026.116507
UR  - https://doi.org/10.1016/j.isci.2026.116507
ER  - 

APA

Eslamifar, O., Soltani, M., & Fatemi, S. M. J. R. (2026). Q-AVOA-net: A multi-domain, quantum-inspired feature-selection and deep-fuzzy framework for interpretable white blood cell classification. iScience. https://doi.org/10.1016/j.isci.2026.116507

Source records