Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach

TL;DR

This paper introduces a lightweight CNN architecture with separable convolutions and SE blocks for efficient and accurate brain tumor classification from MRI images, achieving high accuracy with reduced computational complexity.

Contribution

The novel model combines separable convolutions and SE blocks to improve feature extraction while maintaining efficiency, setting a new benchmark in brain tumor classification.

Findings

Validation accuracy of 99.22%

Test accuracy of 98.44%

Outperforms existing models by 0.5-1% in accuracy

Abstract

Brain tumor classification using MRI images is critical in medical diagnostics, where early and accurate detection significantly impacts patient outcomes. While recent advancements in deep learning (DL), particularly CNNs, have shown promise, many models struggle with balancing accuracy and computational efficiency and often lack robustness across diverse datasets. To address these challenges, we propose a novel model architecture integrating separable convolutions and squeeze and excitation (SE) blocks, designed to enhance feature extraction while maintaining computational efficiency. Our model further incorporates batch normalization and dropout to prevent overfitting, ensuring stable and reliable performance. The proposed model is lightweight because it uses separable convolutions, which reduce the number of parameters, and incorporates global average pooling instead of fully connected layers to minimize computational complexity while maintaining high accuracy. Our model does better than other models by about 0.5% to 1.0% in accuracy and 1.5% to 2.5% in loss reduction, as shown by many experiments. It has a validation accuracy of 99.22% and a test accuracy of 98.44%. These results highlight the model's ability to generalize effectively across different brain tumour types, offering a robust tools for clinical applications. Our work sets a new benchmark in the field, providing a foundation for future research in optimizing the accuracy and efficiency of DL models for medical image analysis.