SEGMO: Brain Tumour Image Segmentation using Hybrid Neural Network Architecture

Abstract

"Brain tumour segmentation from MRI data is crucial for accurate diagnosis and treatment planning, yet it remains challenging due to the heterogeneity and complex spatial structures of tumours. This paper introduces a novel segmentation approach that combines classical neural networks with quantum computing elements, aiming to improve accuracy and robustness in delineating tumour boundaries. By integrating the computational strengths of both paradigms, our method represents a significant advancement in brain tumour segmentation techniques, offering potential for more precise diagnosis and personalized treatment in clinical settings. Utilizing the TransBTS 2020 challenge dataset, known for its extensive collection of annotated MRI scans, we benchmark our hybrid classical-quantum model against traditional CNN architectures. Our findings highlight the limitations of conventional CNNs in capturing the intricate spatial relationships in brain tumour images. In response, we propose a Dilated Groups U-Net architecture that incorporates dilated and grouped convolution techniques to enhance the model's receptive field, significantly improving tumour segmentation accuracy and detail. Crucially, we integrate quantum machine learning within our transfer learning framework, achieving not only enhanced segmentation precision but also a marked reduction in computational time. This innovative approach leverages the parallel processing capabilities of quantum computing, accelerating the training process and potentially enabling more efficient model adaptation to new datasets. Our approach not only demonstrates superior performance over traditional methods but also underscores the promising integration of quantum computing in medical image analysis, setting a foundation for future research and clinical applications."