Multimodal Brain Tumor Detection Using Loss Aware Residual UNet Model

Abstract

This research introduces a novel approach to multimodal brain tumor detection using the Residual UNet architecture. By integrating various imaging techniques, such as MRI and CT, the study offers a comprehensive perspective on brain anomalies. The Residual UNet architecture, an enhancement of the conventional UNet, is tailor-made for biomedical image segmentation. The architecture's residual connections optimize deeper network training, making it suitable for detecting intricate patterns in multimodal brain images. This paper details a structured approach that amalgamates advanced imaging techniques and machine learning to develop an efficient tumor detection system. The study utilizes the BRATS brain tumor MRI datasets and incorporates sophisticated preprocessing, data augmentation, and the innovative Loss-Aware ResUNet model for optimal results. It outperformed other top models with a peak accuracy of 97%.