Multi-EfficientNet ensemble-based brain tumor MRI images classification

Yuhao Lu

doi:10.1117/12.3026352

27 March 2024 Multi-EfficientNet ensemble-based brain tumor MRI images classification

Yuhao Lu

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131050F (2024) https://doi.org/10.1117/12.3026352
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Early screening for tumor symptoms through magnetic resonance imaging (MRI) is crucial for the timely diagnosis and treatment of brain tumor patients. To address the challenge of poor prediction accuracy in single deep learning models, a ensemble learning based brain tumor image classification approach with multiple EfficientNet models is proposed in this paper. Multiple EfficientNet models are integrated, and their feature maps are fused through a spatial channel attention module. Experimental results demonstrate a significant improvement in accuracy compared to other single-model prediction methods. The F1-Score for the four classes, Glioma, Meningioma, Pituitary, and Normal, reached 0.95, 0.95, 0.98, and 0.96, respectively. The proposed method provides robust support for the early diagnosis and treatment of brain tumor patients and holds promising prospects for wide clinical applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuhao Lu "Multi-EfficientNet ensemble-based brain tumor MRI images classification", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131050F (27 March 2024); https://doi.org/10.1117/12.3026352

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KEYWORDS

Brain

Tumors

Neuroimaging

Image classification

Magnetic resonance imaging

Deep learning

Education and training

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