The UNet Model for Multimodal Brain Tumor Classification and Segmentation

R. Prathiba; B. S. Sunitha

doi:10.48084/etasr.11349

Authors

R. Prathiba Department of Computer Science & Engineering, PES Institute of Technology and Management, Shimoga, Karnataka, India | Visvesveraya Technological University, Belagavi, Karnataka, India
B. S. Sunitha Department of Computer Science Engineering (Data Science), PES Institute of Technology and Management, Shimoga Karnataka, India | Visvesveraya Technological University, Belagavi, Karnataka, India

Volume: 15 | Issue: 4 | Pages: 25002-25007 | August 2025 | https://doi.org/10.48084/etasr.11349

Received: 7 April 2025 | Revised: 20 May 2025, 26 May 2025, and 4 June 2025 | Accepted: 6 June 2025 | Online: 7 July 2025

Corresponding author: R. Prathiba

Abstract

MRI manual detection relies on the ability and experience of the physicians. This study presents a brain tumor segmentation and identification system to address this issue and experiments on the BraTS dataset which provides four types of scans, i.e. T1, T2, T1CE, and FAIR, allowing the detection of different class labels and establishing the ground truth for brain tumor segmentation. UNet was used to develop a fully automated approach for the segmentation of glioma on preoperative MRI scans. Images were preprocessed and then classified using the UNet CNN.

Keywords:

brain tumor, BRaTS dataset, CNN, MRI, multimodal, Unet

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