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

TL;DR

This paper introduces a lightweight CNN for multi-class brain tumor classification in MRI images, achieving high accuracy with fewer parameters and lower computational costs, suitable for clinical use.

Contribution

A novel efficient CNN architecture that outperforms existing models in brain tumor classification accuracy and computational efficiency.

Findings

Achieved over 99% accuracy on two datasets.

Outperformed popular pre-trained models in performance and efficiency.

Demonstrated potential for clinical diagnostic support.

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.