Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach
This work addresses the problem of efficient and accurate brain tumor classification for medical diagnostics, representing an incremental improvement over existing methods.
The paper tackles brain tumor classification from MRI images by proposing a lightweight CNN architecture that integrates separable convolutions and squeeze-excitation blocks to balance accuracy and computational efficiency, achieving validation accuracy of 99.22% and test accuracy of 98.44% with improvements of 0.5-1.0% in accuracy and 1.5-2.5% in loss reduction over other models.
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.