Advancing brain tumor MRI classification using SwRD: A parallel swin transformer-ResNet approach

Abstract

Background

Brain tumors are challenging to diagnose and treat, and require accurate and early therapeutic intervention. Magnetic Resonance Imaging (MRI) scans can visualize the internal structure of the brain. Often, deep learning is applied to images for the early and accurate detection of tumor cells. However, these models lack accuracy and efficacy in practical applications. Hybrid or modified models can facilitate better classification and provide insights into early-stage cancer detection.

Methods

This study demonstrates a parallel architecture that uses MRI images and integrates transformer-based frameworks with Convolutional Neural Networks (CNNs) to better classify distinct types of brain tumors. The proposed architecture, SwinResDual (SwRD), combines a Residual Network (ResNet) and a Swin Transformer in parallel to extract key features from input images. Using augmented MRI scans, 31,464 scans for multiclass classification, and 30,000 scans for binary classification, the architecture simultaneously processed images through the ResNet50 and Swin Transformer branches, leveraging their strengths in hierarchical feature extraction and global context modeling to efficiently capture local and global image features. The final classifications are obtained by merging these features and passing them through a linear classifier. This approach identifies strong and varied characteristics and provides a precise brain tumor diagnosis.

Results

In the extensive evaluation, the model performed with an accuracy of 99.79% and a cross-validation accuracy of 100% for multiclass classification, along with 99.97% accuracy in binary classification.

Conclusions

In conclusion, the findings demonstrate great promise for brain tumor detection and advanced medical imaging diagnostics.