TUMbRAIN: A transformer with a unified mobile residual attention inverted network for diagnosing brain tumors from magnetic resonance scans

Abstract

Diagnosing tumors in Magnetic Resonance Imaging (MRI) brain scans is challenging and can lead to errors, even for radiologists. Deep learning, mainly through deep convolutional neural networks, has assisted in automating the diagnosis of these scans. However, there is still room for improvement. Researchers have shown that transformer models hold promise but often remain underutilized due to their need for large amounts of data and complexity compared to traditional neural networks. This paper introduces a new hybrid model called TUMbRAIN (Transformer with a Unified Mobile Residual Attention Inverted Network), which combines a lightweight transformer with a deep convolutional neural network to address these issues. TUMbRAIN incorporates innovative components designed for this purpose, such as the expanded inverted residual block and the unified self-attention mechanism. The results demonstrate that TUMbRAIN outperforms many existing state-of-the-art neural network models, achieving an impressive overall accuracy of 97.94 % with only 1.04 million parameters. These results suggest that hybrid transformer models like TUMbRAIN could significantly advance the automated diagnosis of brain tumors from MRI scans. The study also offers new insights into effectively integrating transformers into traditional neural network architectures, resulting in a cost-effective and accurate deep learning solution for medical imaging. By incorporating these advanced components, TUMbRAIN enhances support for radiological practice through improved diagnostic accuracy and efficiency.