Multimodal Brain Tumour Classification and Segmentation Using a Dual-Attention Swin-UNet with Quantum-Inspired Optimisation for MRI-Based Diagnostics

Abstract

One of the most significant and challenging challenges in medical image processing is the use of magnetic resonance imaging (MRI) to diagnose brain cancers. Significant tumour heterogeneity, irregular morphologies, blurred boundaries, and differences between imaging modalities are the root causes of the problem. Early and precise disease detection is critical for better treatment planning and patient outcomes. Automated brain tumour analysis has greatly improved because of deep learning, particularly convolutional neural networks (CNNs). However, with high-resolution medical images, traditional CNN-based models frequently struggle to identify long-range contextual linkages. This limitation may make it more difficult to obtain precise segmentation and reduce the overall reliability of the diagnosis. Transformer-based designs have recently shown great potential in visualising global relationships. However, because they are hard to train, require a lot of memory, and are rather complex to utilise, their practical application in medical imaging is challenging. We provide a Multimodal Brain Tumour Classification and Segmentation framework based on a Dual-Attention Swin-UNet architecture, enhanced with a Quantum-Inspired Optimisation (QIO) technique, to overcome these limitations. The proposed system successfully integrates fine-grained spatial localisation and global contextual awareness by combining a U-Net-style decoder with an encoder based on Swin Transformer. Dual attention mechanisms spatial and channel attention are added to enhance feature representation and facilitate visualisation of tumour edges, thereby further accelerating feature learning. These attention modules filter away irrelevant background noise, allowing the network to concentrate on clinically significant areas. Additionally, we introduce a quantum-inspired optimisation+ technique to reduce training oscillations and enhance convergence stability, which are frequently observed in transformer-based medical imaging models. A pre-processed version of the BraTS 2020 dataset and additional brain MRI images recorded in HDF5 format are used to train and evaluate the framework. This enables more effective data handling and large-scale training. Due to computational power constraints, GPU acceleration is used for full-scale model training. A real-time, user-friendly web-based diagnostic interface demonstrates a lightweight and deployable inference pipeline. The experimental outcomes demonstrate the strength and clinical practicability of the proposed method, which offers superior tumour segmentation accuracy and reliable classification. The possible development of scalable and intelligent clinical decision support systems for brain tumour diagnosis through the integration of transformer-based architectures, the geography of attention processes, and quantum-inspired optimisation strategies is identified in the study.