Applying Deep Learning to MRI Image Analysis for Brain Tumor Classification

Automated and accurate brain tumor classification from MRI scans is a promising application of deep learning. This paper presents a YOLOv11-based deep learning model for detecting and classifying three tumor types: glioma, meningioma, and pituitary. Performance evaluation demonstrates the model'...

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Bibliographic Details
Published inInternational Conference on Bio-engineering for Smart Technologies (Online) pp. 1 - 5
Main Authors Alamiri, Deimah, Alfadhli, Rimah, Almutairi, Hajar, Alhabshi, Rahimah, Almutairi, Nour, Eleyan, Alaa
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.05.2025
Subjects
Accuracy
Artificial Intelligence
Brain modeling
Brain tumor Detection
Brain tumors
Convergence
Convolutional Neural Networks
Decision making
Deep learning
Explainable AI
Magnetic resonance imaging
MRI Classification
YOLO
Online AccessGet full text
ISSN2831-4352
DOI10.1109/BioSMART66413.2025.11046083

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Summary:Automated and accurate brain tumor classification from MRI scans is a promising application of deep learning. This paper presents a YOLOv11-based deep learning model for detecting and classifying three tumor types: glioma, meningioma, and pituitary. Performance evaluation demonstrates the model's strong capability in tumor detection and classification, particularly with meningioma and pituitary tumors showing higher precision than glioma. Validation curves indicate steady reduction in loss functions across epochs, signifying effective learning and convergence. The model was trained and validated on a structured dataset, achieving a high accuracy performance of 98.9%. Deep learning-based tumor classification can facilitate early detection, assist radiologists, and improve clinical decision-making process.
ISSN:2831-4352
DOI:10.1109/BioSMART66413.2025.11046083