Authors:
Giustino Claudio Miglionico
1
;
Pietro Ducange
1
;
Francesco Marcelloni
1
and
Witold Pedrycz
2
Affiliations:
1
Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy
;
2
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
Keyword(s):
Brain Tumor Classification, Explainable Artificial Intelligence, Deep Learning Learning, Fuzzy Rule-Based Classifiers, Multi-Objective Fuzzy Systems.
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.