Convolutional Neural Network for Binary Brain Tumor Classification Using MRI Scans

Abstract

Brain tumor detection and classification play a critical role in early diagnosis and treatment planning. In this project, we implemented a deep learning-based Convolutional Neural Network (CNN) model to classify MRI brain images into tumor and non-tumor categories. The model was trained on a pre-processed MRI dataset using image normalization and augmentation techniques to improve generalization. Our CNN architecture, consisting of convolutional, pooling, and fully connected layers, achieved a training accuracy of approximately 97–98% and a test accuracy of 92–94%.This work was inspired by the base paper “Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification”, which employed an advanced ensemble fusion approach combining CNNs, Transformers, and clinical data to achieve a classification accuracy of 93.6% with an AUC of 0.967. While our implementation focuses solely on MRI image classification with a single CNN model, the results demonstrate comparable accuracy levels. Unlike the base paper, our model does not incorporate multimodal data fusion or ensemble strategies, making it computationally simpler and more lightweight, while still achieving reliable performance for binary brain tumor detection. The findings indicate that deep learning models can effectively support medical imaging tasks, with scope for future enhancement through multimodal integration, ensemble learning, and advanced evaluation metrics such as precision, recall, and F1-score.