Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network

2026-05-11

AI Summary

This study addresses the challenges of time-consuming manual analysis of brain tumor MRI images and the high computational cost of existing deep learning models, which often struggle with the diversity of tumor types. To overcome these limitations, this work proposes a lightweight and efficient convolutional neural network for multi-class classification of glioma, meningioma, pituitary tumor, and healthy samples. By integrating efficient feature extraction with optimized training strategies, the model achieves remarkable performance while significantly reducing parameter count. On the Figshare and Kaggle datasets, it attains classification accuracies of 99.03% and 99.28%, respectively, with ROC-AUC scores of 99.88% and 99.94%. The proposed approach outperforms mainstream architectures such as DenseNet201 and ResNet50, offering an optimal balance between high accuracy and low computational overhead, thereby demonstrating strong potential for clinical deployment.

Abstract

Improving patient outcomes depends on the prompt and accurate diagnosis of brain tumors, but manual MRI scan analysis is still time-consuming and unreliable. Although deep learning has shown promise, many of the models that are now in use are computationally intensive and have difficulty handling the intrinsic complexity and variety of different types of brain tumors. In this work, we propose a lightweight yet high-performing Convolutional Neural Network (CNN) for multi-class brain tumor classification, employing MRI images to target gliomas, meningiomas, pituitary tumors, and healthy (no tumor) instances. The model was rigorously evaluated on two publicly accessible datasets from Figshare and Kaggle. Leveraging efficient feature extraction and optimized training strategies, our CNN achieved classification accuracies of 99.03% and 99.28%, along with ROC scores of 99.88% and 99.94% on Dataset 1 and Dataset 2, respectively-all while utilizing significantly fewer parameters than popular pre-trained architectures. In contrast to cutting-edge models like DenseNet201, MobileNetV2, VGG19, Xception, InceptionV3, and ResNet50, our approach consistently demonstrated superior performance with reduced computational overhead. These findings highlight the potential of the proposed model as a practical and reliable diagnostic aid in clinical environments.

Problem

• brain tumor classification
• MRI images
• computational efficiency
• multi-class classification
• deep learning

Innovation

• lightweight CNN
• brain tumor classification
• computational efficiency
• MRI image analysis
• multi-class diagnosis