enhancing multiclass brain tumor classification through boosting data with blurred and gaussian filters

This paper introduces a method to enhance classification performance by integrating blurred and gaussian-filtered data into the training process. we demonstrate the efficacy of this approach through comprehensive experiments, revealing improved accuracy, robustness, and generalization compared to traditional boosting techniques. our findings highlight the potential of filtered data augmentation for creating diverse and informative training sets, contributing to more effective adaptation to complex patterns within the data. the proposed method not only enhances accuracy but also exhibits resilience to overfitting, presenting a promising avenue for advancing classification methodologies.