Enhancing Multiclass Brain Tumor Classi cation through Boosting Data with Blurred and Gaussian Filters

This paper introduces a method to enhance classi cation performance byintegrating blurred and Gaussian- ltered data into the training process. We demonstrate thee cacy of this approach through comprehensive experiments, revealing improved accuracy,robustness, and generalization compared to traditional boosting techniques. Our ndingshighlight the potential of ltered data augmentation for creating diverse and informativetraining sets, contributing to more e ective adaptation to complex patterns within the data.The proposed method not only enhances accuracy but also exhibits resilience to over tting,presenting a promising avenue for advancing classi cation methodologies.

Keywords:

Deep learning , Convolutional neural networks (CNNs) , Pretrainednetworks , Filtering techniques.