Implementation of Ensemble Deep Learning for Brain MRI Classification in Tumor Detection

This study proposes an innovative ensemble deep learning framework for the automated classification of brain MRI images aimed at tumor detection. In this research, two state-of-the-art convolutional neural network architectures, ResNet18 and DenseNet121, were individually fine-tuned and trained using a dataset comprising 7,023 images categorized into four classes: glioma, meningioma, no tumor, and pituitary tumor. Extensive data preprocessing, including image resizing to 224×224 pixels, various data augmentation techniques such as random horizontal flipping and rotation, and standard normalization, was applied to improve data quality and enhance model generalizability. Both models demonstrated high validation accuracies of approximately 97.7% when evaluated separately. Moreover, an ensemble approach was implemented by averaging the softmax probability outputs from each model, which led to a superior validation accuracy of 99.36%. This ensemble method effectively combines the complementary strengths of ResNet18 and DenseNet121, thereby reducing misclassification errors and ensuring robust diagnostic performance. The promising results obtained in this study suggest that ensemble deep learning can significantly improve the reliability of automated brain tumor detection, offering a valuable tool for clinical diagnosis. Future research will focus on further optimizing the ensemble strategy through advanced hyperparameter tuning and the seamless integration of additional models to further enhance overall performance.