A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images

Abstract

Serious consequences due to brain tumors necessitate a timely and accurate diagnosis. However, obstacles such as suboptimal imaging quality, issues with data integrity, varying tumor types and stages, and potential errors in interpretation hinder the achievement of precise and prompt diagnoses. The rapid identification of brain tumors plays a pivotal role in ensuring patient safety. Deep learning-based systems hold promise in aiding radiologists to make diagnoses swiftly and accurately. In this study, we present an advanced deep learning approach based on the Swin Transformer. The proposed method introduces a novel Hybrid Shifted Windows Multi-Head Self-Attention module (HSW-MSA) along with a rescaled model. This enhancement aims to improve classification accuracy, reduce memory usage, and simplify training complexity. The Residual-based MLP (ResMLP) replaces the traditional MLP in the Swin Transformer, thereby improving accuracy, training speed, and parameter efficiency. We evaluate the Proposed-Swin model on a publicly available brain MRI dataset with four classes, using only test data. Model performance is enhanced through the application of transfer learning and data augmentation techniques for efficient and robust training. The Proposed-Swin model achieves a remarkable accuracy of 99.92%, surpassing previous research and deep learning models. This underscores the effectiveness of the Swin Transformer with HSW-MSA and ResMLP improvements in brain tumor diagnosis. This method introduces an innovative diagnostic approach using HSW-MSA and ResMLP in the Swin Transformer, offering potential support to radiologists in timely and accurate brain tumor diagnosis, ultimately improving patient outcomes and reducing risks.