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Ensemble Deep Learning for Multi-Class Brain Tumour Classification: Integrating ResNet, Inception, and EfficientNet Architectures

Crossref DOI link: https://doi.org/10.35940/ijsce.F3709.15060126

Published Online: 2026-01-30

Update policy: https://doi.org/10.35940/beiesp.crossmarkpolicy

Authors

,

Devarapalli, Dr. Dharmaiah https://orcid.org/0000-0002-5804-5880
Ajay, Chinthamaani https://orcid.org/0009-0008-5337-2226
,

R. Vignan, https://orcid.org/0009-0005-1701-1026
,

Vardhan, Karri Harsha https://orcid.org/0009-0005-4955-4068
,

Naidu, Meesala Siddharth https://orcid.org/0009-0002-3233-4139
,
More Information

Published On

Journal Name: International Journal of Soft Computing and Engineering (IJSCE)

Publisher By

Publisher Name: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)

Declaration

Conflicts of Interest: Based on my understanding, this article has no conflicts of interest.

Funding Support: This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted objectively and without external influence.

Ethical Approval and Consent to Participate: The content of this article does not necessitate ethical approval or consent to participate with supporting documentation.

Data Access Statement and Material Availability: The Brain Tumour Classification MRI dataset used in this study is publicly available.

Authors Contributions: All authors participated equally in this research. Dharmaiah Devarapalli supervised the study and provided guidance. Chinthamaani Ajay and R. Vignan contributed to implementation, experimentation, and manuscript preparation. All authors reviewed and approved the final manuscript.