Hybrid Convolutional-Recurrent Neural Network Architecture with Attention Mechanisms for Multi-modal Medical Image Segmentation

Abstract

In the realm of multi-modal medical image segmentation, this investigation centers its focus on brain and liver tumors. Its core mission is to birth a pioneering Hybrid Convolutional-Recurrent Neural Network (CRNN) architecture, turbocharged with Attention Mechanisms, in a quest to heighten the precision of tumor delineation. In the intricate landscape of medical imaging, where accuracy is the linchpin of diagnosis and treatment, this research carries substantial weight. Its significance transcends the laboratory, venturing into the realm of practicality. By harmoniously blending convolutional and recurrent neural networks, this innovation propels the evolution of deep learning in medical imaging. The outcome unveils a superior performance, poised to revolutionize the clinical frontiers of radiology and oncology. The urgency of rapid and precise tumor identification in these domains cannot be overstated. Moreover, this exploration harnesses the power of the BraTS (Multimodal Brain Tumor Segmentation Challenge) dataset. This dataset, a meticulously annotated treasure trove of T1-weighted, T2-weighted, FLAIR, and T1 post-contrast MRI scans, underscores the research's relevance and potential to reshape healthcare outcomes through cutting-edge medical image analysis.