Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis

<p>Early detection of brain tumors is particularly important, as&nbsp;brain tumors are one of the leading causes of cancer-related mortality.&nbsp;However, identifying brain tumors can be challenging due to differences&nbsp;in tumor tissue variation among patients and, in some cases, the similarity&nbsp;of tumors to normal tissue. In this article, we propose a novel,<br>resource-efficient simulator called Brain Analysis for fast and accurate&nbsp;analysis and verification of brain tumors. The proposed simulator aims&nbsp;to improve the reliability and speed of this process in treatment. To evaluate&nbsp;its performance, accuracy, and other important factors, we compare&nbsp;the proposed algorithm with several other methods, including a genetic&nbsp;algorithm, a CNN-based multi-classification model, an ML scheme +&nbsp;SVM approach, and a CapsNets model based on collective intelligence.&nbsp;Our experimental results show that the proposed algorithm significantly&nbsp;reduces the time needed to accurately detect early brain tumors, compared&nbsp;to the other methods, by using a multi-core architecture and appropriate&nbsp;filters in the Brain Analysis Simulator. This research objective&nbsp;demonstrates the potential of the proposed simulator as a component of&nbsp;a strategy for the early and faster detection of brain tumors.</p>