Image Preprocessing and Optimizer Sensitivity: Implications for Convolutional Neural Networks in Diagnosing Brain...

Brain tumor classification using magnetic resonance imaging (MRI) presents challenges due to variations in tumor size, shape, and texture. Although traditional image preprocessing methods are commonly employed to improve input quality, their impact on optimizer behavior and CNN performance has yet to be thoroughly investigated. This research examines the effect of preprocessing on convergence, generalization, and classification accuracy across various optimizers. We utilize a publicly available Kaggle dataset to create two preprocessing pipelines: a baseline pipeline that only resizes images and a traditional pipeline that converts images to grayscale, blurs them, and applies morphological filtering. We then test how these pipelines affect three optimizers: Adam, Root Mean Square Propagation (RMSProp), and Stochastic Gradient Descent (SGD). To separate protocol variables, a fixed CNN architecture is used throughout. Performance is assessed using accuracy, precision, recall, and F1-score, validated through five-fold cross-validation. Results show that baseline preprocessing consistently yields higher accuracy and more stable convergence across all optimizers, with RMSProp and SGD achieving the highest mean accuracy of 99.53% under five-fold cross-validation. The findings address the understudied effect of preprocessing on optimizer performance, emphasizing the need for preprocessing-aware training strategies to improve robustness and interpretability in medical image analysis.