Early stage brain tumor prediction using dilated and Attention-based ensemble learning with enhanced Artificial rabbit optimization for brain data

Abstract

The integration of deep learning into brain data analysis has notably boosted the field of biomedical data analysis. In the context of intricate conditions like cancer, various data modalities can reveal distinct disease characteristics. Brain data has the potential to expose additional insights compared to using the data sources in isolation. Moreover, techniques are selected and prioritized based on the speed and accuracy of the data. Therefore, a new deep learning technique is assisted in predicting the brain tumor from the brain data to provide accurate prediction outcomes. The brain data required for predicting the brain tumor is garnered through various online sources. Then, the collected data are applied to the data preprocessing phase for cleaning the collected brain data and then applied to the data transformation method to improve the efficiency for providing better decision-making over prediction. The transformed data is then offered to the weighted feature selection process, where the weights of the features are optimized through the proposed Enhanced Artificial Rabbits Optimizer. The selection of weighted features is primarily adopted for solving the data dimensionality issues and these resultant features are given to the Dilated and Attention-based Ensemble Learning Network to provide the effective prediction outcome, where the deep learning structures like 1-Dimensional Convolutional Neural Networks, Bidirectional Long Short-Term Memory (BiLSTM), Deep Temporal Convolution Network are ensembled in the DAEL network. Finally, the prediction outcome attained from the proposed model is validated through the existing brain tumor prediction frameworks to ensure the efficacy of the implemented scheme.