A Novel Ensemble Model for Brain Tumor Diagnosis

Amira Samy Talaat ()
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Amira Samy Talaat: Computers and Systems Department, Electronics Research Institute, Cairo 12622, Egypt

New Mathematics and Natural Computation (NMNC), 2025, vol. 21, issue 01, 195-211

Abstract: The spread of brain tumors resulted in numerous deaths, and cancer patients are still being treated. Four novel models are introduced and compared in this study. The best one is the PIEnsemble model, which was created to correctly identify and classify MRI images for brain tumor classification. The PIEnsemble primarily combines three deep learning techniques, ResNet Model, GoogleNet Model, and Inception Model, integrated with two dimensionality reduction techniques, PCA and ICA, which are used for feature extraction with a combination of Linear, Batch1Norm1D, and ReLU layers. Simulations in a series on two benchmarks datasets were run to show the improved effectiveness of the PIEnsemble model. The experimental results highlighted the improvements to the PIEnsemble classifier structure, which has the highest classification accuracy ratio and superior performance to other methods, with a 98.25% accuracy for Dataset 1 and a 98.75% accuracy for Dataset 2.

Keywords: Tumor of brain diagnose; deep learning; PCA; ICA; classification; feature extraction; artificial intelligence (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S1793005725500115

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