Transfer Learning Models for MRI-Based Brain Tumor Detection

Human health, especially brain health, is paramount for overall well-being. Magnetic Resonance Imaging (MRI) serves as a vital diagnostic tool for assessing critical organs such as the brain. The abundance of data generated by MRI scans presents a significant opportunity for artificial intelligence...

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Bibliographic Details
Published in:	2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP) Vol. 1; pp. 14 - 19
Main Authors:	Berete, Moriba, Echtioui, Amira, Sellami, Lamia, Hamida, Ahmed Ben
Format:	Conference Proceeding
Language:	English
Published:	IEEE 11-07-2024
Subjects:	Accuracy Artificial intelligence Binary classification Brain modeling Brain tumor Image processing Magnetic resonance imaging Measurement Transfer learning
Online Access:	Get full text
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Summary:	Human health, especially brain health, is paramount for overall well-being. Magnetic Resonance Imaging (MRI) serves as a vital diagnostic tool for assessing critical organs such as the brain. The abundance of data generated by MRI scans presents a significant opportunity for artificial intelligence (AI) to aid in health decision-making processes. In this paper, we focus on using AI techniques, particularly transfer learning, to classify brain tumors from MRI images. By leveraging transfer learning, we aim to enhance the performance of image processing algorithms in the classification of brain tumors. Our approach involves employing established architectures such as Xception, ResNet50, and DenseNet201, adapted through transfer learning for this specific task. Among the architectures tested, our results highlight the superiority of the ResNet50 model in binary classification of brain tumors using MRI scans. The proposed model achieves remarkable performance metrics, including an accuracy of 99 \%, precision of 99.5 \%, recall of 99.5 \%, and F1 Score of 99 \%. This research underscores the potential of transfer learning in advancing diagnostic capabilities for brain health assessment.
ISSN:	2687-878X
DOI:	10.1109/ATSIP62566.2024.10639035