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

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 explain- ability. Thanks to their straightforward structure, FRBCs provide reliable predictions with comprehensible linguistic rules, essential for medical decision-making.