Brain Tumor Classification of MRI Dataset Using Ensemble Learning with EfficientNetV2 and ViT-B16

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : Bora Pavani, Raghunath Mandipudi, Siva Suresh Kumar Gandi, Pothina Mohnish Sabarinath, Yada Sunitha

10.14445/23488379/IJEEE-V12I3P103

How to Cite?

Bora Pavani, Raghunath Mandipudi, Siva Suresh Kumar Gandi, Pothina Mohnish Sabarinath, Yada Sunitha, "Brain Tumor Classification of MRI Dataset Using Ensemble Learning with EfficientNetV2 and ViT-B16," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 3, pp. 21-35, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I3P103

Abstract:

Unidentified and untreated brain cancer can be fatal. Radiologists routinely use pictures from MRIs and CT scans to make early diagnoses of brain disorders. Assessing the border of the brain tumor on MRI scans and figuring out its potential pathology are crucial stages in catching this dangerous condition early on. We categorize and segment brain tumors based on features such as consistency, uneven borders, and arrangement. Disparities between observers and such substantial deviations can lead to serious issues during neurosurgical procedures. On the other hand, it can be difficult at low-income medical facilities to not have radiologists to review medical images. Machine learning-based automatic analysis of medical images may be able to help with diagnosis in order to solve the problem at hand. The importance of Magnetic Resonance Imaging (MRI) in detecting and managing brain malignancies has increased exponentially. Given the complexity and diversity of tumor features, accurately classifying brain tumors from MRI images remains a challenging task. This article talks about how EfficientNet V2 and ViT B16-powered ensemble models can be used to sort tumor cells into different groups. The Geometric Average Ensemble Model that was created was 95% accurate compared to other implementations. It was trained on data from 700 MRI images of brain tumors and then tested on 281 images. The study’s results show a clearer enhancement in image classification of brain tumors than in previous studies.

Keywords:

Brain cancer, Deep Learning, ViT-B16, Ensemble model, Machine Learning, EfficientNet V2, Brain tumor classification.

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