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Brain Tumor Classification using EfficientNet

by Md Masum Billah, Rashad Bakhshizada, Denesh Das, Tasmita Tanjim Tanha, Rashedur Rahman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 86
Year of Publication: 2026
Authors: Md Masum Billah, Rashad Bakhshizada, Denesh Das, Tasmita Tanjim Tanha, Rashedur Rahman
10.5120/ijca2026926493

Md Masum Billah, Rashad Bakhshizada, Denesh Das, Tasmita Tanjim Tanha, Rashedur Rahman . Brain Tumor Classification using EfficientNet. International Journal of Computer Applications. 187, 86 ( Mar 2026), 66-71. DOI=10.5120/ijca2026926493

@article{ 10.5120/ijca2026926493,
author = { Md Masum Billah, Rashad Bakhshizada, Denesh Das, Tasmita Tanjim Tanha, Rashedur Rahman },
title = { Brain Tumor Classification using EfficientNet },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2026 },
volume = { 187 },
number = { 86 },
month = { Mar },
year = { 2026 },
issn = { 0975-8887 },
pages = { 66-71 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number86/brain-tumor-classification-using-efficientnet/ },
doi = { 10.5120/ijca2026926493 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-03-20T22:55:06.682736+05:30
%A Md Masum Billah
%A Rashad Bakhshizada
%A Denesh Das
%A Tasmita Tanjim Tanha
%A Rashedur Rahman
%T Brain Tumor Classification using EfficientNet
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 86
%P 66-71
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accurate classification of brain tumors from magnetic resonance imaging (MRI) is essential for assisting clinical diagnosis and treatment planning. This study presents a deep learning–based approach for brain tumor classification using the EfficientNetB3 architecture. Transfer learning with initialization from weights learned on ImageNet is used, and the network is fine-tuned on a brain MRI dataset containing four classes: glioma, meningioma, pituitary tumor, and no tumor. The proposed system learns end to end to produce discriminative features from an image. Experimental results show that EfficientNetB3 achieves a test accuracy of 99%, with macro-averaged precision, recall (sensitivity), and F1-score of 99%. These results demonstrate the effectiveness of EfficientNetB3 for reliable and high-performance brain tumor classification.

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Index Terms

Computer Science
Information Sciences

Keywords

Deep learning MRI brain tumor classification EfficientNetB3 medical imaging

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