RU-Net2+: A Deep Learning Algorithm for Accurate Brain Tumor Segmentation and Survival Rate Prediction

Abstract

Brain tumors present a significant medical concern, posing challenges in both diagnosis and treatment. Deep learning has emerged as an evolving technique for automating the diagnostic process for brain tumors. This research paper introduces a novel deep-learning framework designed explicitly for brain tumor diagnosis. The framework encompasses various tasks: tumor detection, classification, segmentation, and survival rate prediction. The framework was applied to the BraTS dataset, an extensive collection of brain tumor images, to evaluate its effectiveness. The proposed workflow initiates with data acquisition, followed by an enhancement of this data using a Convolutional Normalized Mean Filter (CNMF) during pre-processing. This prepares the data for the multi-class classification performed using the novel DBT-CNN classifier model. For precise tumor demarcation, the RU-Net2+ model is employed, yielding segmented regions from which features are subsequently extracted utilizing the Cox model. These extracted features play a pivotal role in the final step, where the survival rate of patients is predicted using a logistic regression model. The experimental results showcased the exceptional performance of the proposed framework, surpassing current benchmarks in classification accuracy, tumor segmentation precision, and survival rate prediction. For high-grade glioma (HGG) tumors, the framework achieved an impressive classification accuracy of 99.51%, while for low-grade glioma (LGG) tumors, the accuracy reached 99.28%. The accuracy of tumor segmentation stood at 98.39% for HGG tumors and 99.1% for LGG tumors. The RU-Net2+ algorithm accurately predict patient survival rates: 85.71% long-term, 72.72% medium-term, and 61.54% short-term, corresponding Mean Squared Errors of 0.13, 0.21, and 0.31. These results provide valuable insights for medical professionals making brain tumor treatment decisions. Additionally, the framework shows promise for automating brain tumor diagnosis and enhancing patient care.