Enhancing brain tumor classification with a simplified CNN through hyperparameter optimization.
Authors
Affiliations (2)
Affiliations (2)
- Universite Hassan 1er, University Hassan 1er, Settat, Chaouia-Ouardigha, 26000, Morocco.
- Tunisia Polytechnic School, Ecole Polytechnique de Tunisie, La Marsa, 2078, Tunisia.
Abstract
Brain tumors remain a major public health challenge because of their high mortality rate and the need for timely and accurate diagnosis. Magnetic Resonance Imaging (MRI) is widely used for brain tumor assessment, yet conventional diagnostic workflows still depend largely on the subjective interpretation of radiologists, which may lead to inter-observer variability. In this context, deep learning approaches-particularly Convolutional Neural Networks (CNNs)-have gained increasing attention for automated medical image analysis, as they are capable of learning discriminative features directly from imaging data and improving classification accuracy. Nevertheless, the performance of CNN-based models is strongly influenced by the choice of hyperparameters, whose optimization remains a non-trivial task due to complex and non-linear interactions within the network.This study investigates the impact of different hyperparameter optimization strategies on the performance of a simplified CNN model for brain tumor classification from MRI images. Three widely used optimization methods, Genetic Algorithms, Bayesian Optimization, and HyperBand-are considered and systematically compared. To emphasize efficiency and practical applicability, the CNN architecture is deliberately kept lightweight, aiming to reduce computational complexity while preserving sufficient modeling capacity. Experiments are carried out on a publicly available brain MRI dataset that includes glioma, meningioma, pituitary tumor, and healthy subjects.To account for performance variability and ensure statistical reliability, each optimized model is evaluated over ten independent runs using different random initializations. The reported results therefore include both the mean performance values and the corresponding standard deviations.
The experimental analysis shows that the Genetic Algorithm-based approach achieves a mean accuracy of 96.41\% ± 0.36\%, while Bayesian Optimization improves this performance to 97.37\% ± 0.30\%. Among the evaluated strategies, HyperBand yields the highest average accuracy of 97.73\% ± 0.48\%, indicating its ability to consistently identify effective hyperparameter configurations for the proposed CNN architecture.