Deep Learning and Multi-Objective Evolutionary Fuzzy Classifiers: A Comparative Analysis for Brain Tumor Classification in MRI Images

Giustino Claudio Miglionico, Pietro Ducange, Francesco Marcelloni, Witold Pedrycz

2024

Abstract

This paper presents a comparative analysis of Deep Learning models and Fuzzy Rule-Based Classifiers (FBRCs) for Brain Tumor Classification from MRI images. The study considers a publicly available dataset with three types of brain tumors and evaluates the models based on their accuracy and complexity. The study involves VGG16, a convolutional network known for its high accuracy, and FBRCs generated via a multi-objective evolutionary learning scheme based on the PAES-RCS algorithm. Results show that VGG16 achieves the highest classification performance but suffers from overfitting and lacks interpretability, making it less suitable for clinical applications. In contrast, FBRCs, offer a good balance between accuracy and explainability. Thanks to their straightforward structure, FRBCs provide reliable predictions with comprehensible linguistic rules, essential for medical decision-making.

Paper Citation

in Harvard Style

Miglionico G., Ducange P., Marcelloni F. and Pedrycz W. (2024). Deep Learning and Multi-Objective Evolutionary Fuzzy Classifiers: A Comparative Analysis for Brain Tumor Classification in MRI Images. In Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS; ISBN 978-989-758-720-7, SciTePress, pages 108-115. DOI: 10.5220/0012940500003886

in Bibtex Style

@conference{explains24,
author={Giustino Claudio Miglionico and Pietro Ducange and Francesco Marcelloni and Witold Pedrycz},
title={Deep Learning and Multi-Objective Evolutionary Fuzzy Classifiers: A Comparative Analysis for Brain Tumor Classification in MRI Images},
booktitle={Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS},
year={2024},
pages={108-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012940500003886},
isbn={978-989-758-720-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS
TI - Deep Learning and Multi-Objective Evolutionary Fuzzy Classifiers: A Comparative Analysis for Brain Tumor Classification in MRI Images
SN - 978-989-758-720-7
AU - Miglionico G.
AU - Ducange P.
AU - Marcelloni F.
AU - Pedrycz W.
PY - 2024
SP - 108
EP - 115
DO - 10.5220/0012940500003886
PB - SciTePress