Brain Tumor Growth Prediction Based on Segmentation Using Resolution Convolution Network

Abstract

This study presents a novel approach to brain tumor growth prediction using Resolution Convolution Network (RCN) segmentation. The method involves precise segmentation of brain tumor regions from magnetic resonance imaging (MRI) scans followed by application of RCN to predict growth. The RCN architecture integrates high-resolution convolutional layers, which facilitates the extraction of detailed features that are crucial for accurate segmentation and subsequent prediction. Performance evaluation is performed on a comprehensive dataset of MRI brain scans