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

Summary

This study employs a controlled framework to evaluate preprocessing pipelines and optimizers within a fixed architecture, aiming to determine how classical preprocessing affects optimizers and Convolutional Neural Networks (CNNs) in brain tumor classification.

Abstract

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.

Introduction

Brain tumor classification using magnetic resonance imaging (MRI) is a crucial task in neuro-oncology, where early and accurate diagnosis directly influences treatment planning and patient outcomes¹. CNNs have become the dominant approach for automating this process due to their ability to learn hierarchical spatial and textural features directly from raw image data². However, the quality of input data remains a key determinant of model performance. Classical preprocessing techniques-such as grayscale conversion, Gaussian blurring, thresholding, and morphological operations-are routinely applied to reduce noise and empha....

Protocol

Figure 1 shows an overview of the Protocol workflow. This study examines the effect of classical image preprocessing on the performance of CNNs and the behavior of optimizers in brain tumor classification using MRI. The protocol encompasses dataset preparation, dual path preprocessing pipelines, model architecture, optimizer configuration, performance evaluation, and interpretability validation. All experiments were executed in Python 3.10.12 using Keras version 2.13.1 with TensorFlow backend, OpenCV version 4.8.0, and Matplotlib version 3.8.0.

Dataset preparation
The BR35H brain tumor MR....

Results

Adam optimizer - Baseline preprocessing:
Figure 2 illustrates the performance of a brain tumor classification model using the Adam optimizer with baseline preprocessing. The confusion matrix shows near-perfect separation between tumorous and non-tumorous cases, with only 8 misclassifications out of 600 samples. The accompanying classification report confirms this with precision, recall, and F1-scores all at or above 0.98 for both classes.

Discussion

The success of CNN-based brain tumor classification in this study was primarily driven by two protocol components: preprocessing design and optimizer selection. Baseline preprocessing -- consisting solely of image resizing -- preserved native pixel intensity and spatial structure, enabling the model to learn clinically relevant features. In contrast, traditional preprocessing methods (such as grayscale conversion, Gaussian blur, thresholding, and morphological operations) introduced feature abstraction, which often suppr.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
API Wrapper	Keras	2.13.1(RRID:SCR_016345)	High-level API for CNN architecture and training
Attribution Tool	Grad-CAM Implementation	Custom (via Keras)	Visual explanation of CNN attention
BR35H Brain Tumor MRI Dataset	Kaggle	https://www.kaggle.com/ahmedhamada0/brain-tumor-detection	Source of labeled MRI images for classification
Brain Tumor Dataset	Ultralytics	https://docs.ultralytics.com/datasets/detect/brain-tumor/
Deep Learning Library	TensorFlow	2.15.0 (RRID:SCR_018345)	Backend for CNN model implementation
Image Processing	OpenCV	4.8.0 (RRID:SCR_015526)	Preprocessing: grayscale, blur, threshold, morphology
Programming Language	Python	3.10.12 (RRID:SCR_008394)	Execution environment for all experiments
Visualization	Matplotlib	3.8.0 (RRID:SCR_008624)	Plotting loss curves and Grad-CAM overlays