Atıf Formatları
Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches
  • IEEE
  • ACM
  • APA
  • Chicago
  • MLA
  • Harvard
  • BibTeX

E. Akpinar Et Al. , "Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches," Neural Computing and Applications , 2025

Akpinar, E. Et Al. 2025. Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches. Neural Computing and Applications .

Akpinar, E., Islam, S. M. N., & ODUNCUOĞLU, M., (2025). Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches. Neural Computing and Applications .

Akpinar, Emine, Sardar M. N. Islam, And Murat ODUNCUOĞLU. "Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches," Neural Computing and Applications , 2025

Akpinar, Emine Et Al. "Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches." Neural Computing and Applications , 2025

Akpinar, E. Islam, S. M. N. And ODUNCUOĞLU, M. (2025) . "Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches." Neural Computing and Applications .

@article{article, author={Emine Akpinar Et Al. }, title={Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches}, journal={Neural Computing and Applications}, year=2025}