Improved Brain Tumour Prognosis Using Deep Neural Networks

The segmentation of brain tumor in MRI is an essential step to specify the most likely location, stage and type of a lesion being diagnosed by physician before any medical treatment planning with full-confidence for better prognosis. Traditional manual annotation is expensive, labor-intensive and inconsistent. Deep-learning-oriented image analysis has recently brought new impetus to this quest, as for example in the form of convolutional neural network (CNN) based models which offer some advantages over traditional methods, but may have difficulty in capturing more complex spatial relations. This paper proposes a novel framework for brain tumor segmentation using Dynamic Graph Neural Networks (DGNNs) and Dense Micro-block Difference Feature (DMDF) extraction. Here, DGNNs build and perform operations on graphs which enables them to learn complex spatial as well as contextual features. The DMDF improves feature representation by amplifying regional/micro-block differences on which accurate tumor boundary definition crucially depends. The proposed model integrates DGNNs with a fully convolutional neural network (FCNN), processing multi-modal MRI scans to generate precise segmentation masks. The training pipeline includes robust data preprocessing, normalization, and augmentation techniques for improved generalization. Evaluation on the Brain MRI Images for Brain Tumor Detection dataset demonstrates the effectiveness of this approach, achieving high Dice similarity coefficients and accurate segmentation performance. This method significantly enhances the accuracy and efficiency of brain tumor diagnosis and treatment planning.