脑肿瘤，通常称为颅内肿瘤，是由快速增殖的细胞产生的异常组织肿块。在医学成像过程中，将脑肿瘤与健康组织分开至关重要。本文的目的是提高医学成像中将肿瘤区域与健康组织分离的准确性，特别是针对MRI图像中的脑肿瘤，这在医学图像分析领域是困难的。在我们的研究工作中，我们提出了 IC-Net（Inverted-C），这是一种新颖的语义分割架构，它结合了来自各种模型的元素，以提供有效且精确的结果。该架构包括多注意力（MA）块、特征串联网络（FCN）、注意力块，它们执行改善脑肿瘤分割的关键任务。 MA-block聚合了多注意力特征以适应不同的肿瘤大小和形状。注意力块专注于关键区域，从而在复杂图像中实现更有效的分割。 FCN-block 捕获了不同的特征，使模型对脑肿瘤图像的各种特征更加鲁棒。我们提出的架构用于加速训练过程，并解决脑肿瘤图像的多样性带来的挑战，最终可能提高分割性能。 IC-Net 显着优于典型的 U-Net 架构和其他当代有效的分割技术。在 BraTS 2020 数据集上，我们的 IC-Net 设计在准确度、损失、特异性、灵敏度方面取得了显着的成果，分别为 99.65、0.0159、99.44、99.86 和 DSC（核心肿瘤、整体肿瘤和增强肿瘤分别为 0.998717、0.888930、0.866183）。© 2024。作者。

Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise from rapidly multiplying cells. During medical imaging, it is essential to separate brain tumors from healthy tissue. The goal of this paper is to improve the accuracy of separating tumorous regions from healthy tissues in medical imaging, specifically for brain tumors in MRI images which is difficult in the field of medical image analysis. In our research work, we propose IC-Net (Inverted-C), a novel semantic segmentation architecture that combines elements from various models to provide effective and precise results. The architecture includes Multi-Attention (MA) blocks, Feature Concatenation Networks (FCN), Attention-blocks which performs crucial tasks in improving brain tumor segmentation. MA-block aggregates multi-attention features to adapt to different tumor sizes and shapes. Attention-block is focusing on key regions, resulting in more effective segmentation in complex images. FCN-block captures diverse features, making the model more robust to various characteristics of brain tumor images. Our proposed architecture is used to accelerate the training process and also to address the challenges posed by the diverse nature of brain tumor images, ultimately leads to potentially improved segmentation performance. IC-Net significantly outperforms the typical U-Net architecture and other contemporary effective segmentation techniques. On the BraTS 2020 dataset, our IC-Net design obtained notable outcomes in Accuracy, Loss, Specificity, Sensitivity as 99.65, 0.0159, 99.44, 99.86 and DSC (core, whole, and enhancing tumors as 0.998717, 0.888930, 0.866183) respectively.© 2024. The Author(s).