Deep fusion of multi-modal features for brain tumor image segmentation

Accurate segmentation of pathological regions in brain magnetic resonance images (MRI) is essential for the diagnosis and treatment tumors. Multi-modality MRIs, which offer diverse feature information, are commonly utilized tumor image segmentation. Deep neural networks have become prevalent this field; however, many approaches simply concatenate different modalities input them directly into network segmentation, disregarding unique characteristics complementarity each modality. In study, we propose a method that leverages deep residual learning with multi-modality fusion. Our approach involves extracting fusing distinct complementary features from various modalities, fully exploiting information within convolutional to enhance performance We evaluate effectiveness our proposed using BraTS2021 dataset demonstrate fusion significantly improves accuracy. achieves competitive results, Dice values 83.3, 89.07, 91.44 enhanced tumor, core, whole respectively. These findings highlight potential improving through accurate MRIs.