Back to Search Start Over

Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks.

Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks.

Authors :
Martínez-Del-Río-Ortega, Rafael
Civit-Masot, Javier
Luna-Perejón, Francisco
Domínguez-Morales, Manuel
Source :
Big Data & Cognitive Computing; Sep2024, Vol. 8 Issue 9, p123, 17p
Publication Year :
2024

Abstract

Early and precise detection of brain tumors is critical for improving clinical outcomes and patient quality of life. This research focused on developing an image classifier using convolutional neural networks (CNN) to detect brain tumors in magnetic resonance imaging (MRI). Brain tumors are a significant cause of morbidity and mortality worldwide, with approximately 300,000 new cases diagnosed annually. Magnetic resonance imaging (MRI) offers excellent spatial resolution and soft tissue contrast, making it indispensable for identifying brain abnormalities. However, accurate interpretation of MRI scans remains challenging, due to human subjectivity and variability in tumor appearance. This study employed CNNs, which have demonstrated exceptional performance in medical image analysis, to address these challenges. Various CNN architectures were implemented and evaluated to optimize brain tumor detection. The best model achieved an accuracy of 97.5%, sensitivity of 99.2%, and binary accuracy of 98.2%, surpassing previous studies. These results underscore the potential of deep learning techniques in clinical applications, significantly enhancing diagnostic accuracy and reliability. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
25042289
Volume :
8
Issue :
9
Database :
Complementary Index
Journal :
Big Data & Cognitive Computing
Publication Type :
Academic Journal
Accession number :
180017176
Full Text :
https://doi.org/10.3390/bdcc8090123