Brain Tumor Detection Using MRI and Intelligent Systems: A Comprehensive Review of Challenges and Research Opportunities

A Graph Attention Network (GAT) encoder is then used to model the structural relationship between sensor nodes. The attention mechanism gives significance to those neighboring nodes that assist the system to identify trusted neighboring nodes and the optimal communication routes.

GRU layer is added to predict temporal dependencies because network conditions like node movement and energy vary. Another refinement layer focusing on attention is used to emphasize important features, which relate to energy management, evaluation of trust, and reliability of communication. The framework also ends with two prediction branches which collectively carry out the functions of cluster head selection and identifying the path which all the routing paths take, to be able to make coordinated and efficient decisions in the IoT network.

This paper will conduct a review of the current research trends in automated brain tumor detection and pinpoint some challenges that exist regarding existing deep learning models. The analysis shows that there is a necessity to develop integrated and computationally efficient diagnostic frameworks that can enhance the contextual feature representation and assist in practice in the context of intelligent healthcare systems.