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AI Driven Brain Tumor Segmentation using U-Net: A Deep Learning Approach

by Okunade Temilola, Amusu Mary I., Aremu Idris A., Tiamiyu Olalekan S., Ganiu Serifat, Davies Muyis
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 59
Year of Publication: 2025
Authors: Okunade Temilola, Amusu Mary I., Aremu Idris A., Tiamiyu Olalekan S., Ganiu Serifat, Davies Muyis
10.5120/ijca2024924228

Okunade Temilola, Amusu Mary I., Aremu Idris A., Tiamiyu Olalekan S., Ganiu Serifat, Davies Muyis . AI Driven Brain Tumor Segmentation using U-Net: A Deep Learning Approach. International Journal of Computer Applications. 186, 59 ( Jan 2025), 1-5. DOI=10.5120/ijca2024924228

@article{ 10.5120/ijca2024924228,
author = { Okunade Temilola, Amusu Mary I., Aremu Idris A., Tiamiyu Olalekan S., Ganiu Serifat, Davies Muyis },
title = { AI Driven Brain Tumor Segmentation using U-Net: A Deep Learning Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 59 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number59/ai-driven-brain-tumor-segmentation-using-u-net-a-deep-learning-approach/ },
doi = { 10.5120/ijca2024924228 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-03T00:30:36.796759+05:30
%A Okunade Temilola
%A Amusu Mary I.
%A Aremu Idris A.
%A Tiamiyu Olalekan S.
%A Ganiu Serifat
%A Davies Muyis
%T AI Driven Brain Tumor Segmentation using U-Net: A Deep Learning Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 59
%P 1-5
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain tumors are brought on by the growth of aberrant cells in an unfavorable region of the brain. There are two types: malignant tumors, which are more aggressive and carcinogenic and can spread to other parts of the body, and benign tumors, which are less aggressive and non-cancerous. One of the deadliest types of cancer, a brain tumor is a life-threatening condition. Early identification and precise segmentation constitute the first step in the treatment of brain tumors. The most used medical imaging technique for assessing brain tumors is MRI (Magnetic Resonance Imaging). Manual segmentation requires a lot of time and effort and is subject to human error and judgment. An automatic segmentation technique based on U-Net, a Convolutional Neural Network (CNN), was proposed in this study, developed for biomedical imaging. The BraTS 2021 dataset, which includes three-dimensional MRI images in four different modalities (T1, T1ce, T2, and T2 Flair, each with four labels), was used to train and test the network. The final model has a 99.4 percent accuracy rate.

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Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation MRI Brain tumor CNN non-cancerous

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