Magnetic resonance imaging (MRI) scan analysis is an effective tool that accurately detects abnormal brain tissue. This manuscript proposes the strategy of segmentation of brain tumors in MRI images and uses the technique of weighted fuzzy factor based on kernel metrics. Here, a deep auto encoder (DAE) with barnacle mating algorithm (BMOA) and random forest (RF) classifier are used to tumor stage classification to enhance the accuracy of prediction. This manuscript presents a deep-neural network structure, integrating DAE and RF, with a classification unit, which is used for the classification of brain MRI. Finally, the segmented features are graded by the DAE with BMOA and RF. The proposed method is executed in MATLAB site and the performance is analyzed with existing methods. The experimental outcomes of the proposed method are assessed and validated in MR brain images depending on accuracy, sensitivity, and specificity for performance with quality analysis.

中文翻译：

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使用加权模糊聚类算法、具有藤壶交配算法和随机森林分类器技术的深度自动编码器自动检测和分类脑肿瘤

磁共振成像（MRI）扫描分析是准确检测异常脑组织的有效工具。这篇手稿提出了 MRI 图像中脑肿瘤的分割策略，并使用了基于核度量的加权模糊因子技术。在这里，具有藤壶交配算法（BMOA）和随机森林（RF）分类器的深度自动编码器（DAE）用于肿瘤分期分类，以提高预测的准确性。这份手稿提出了一个深度神经网络结构，集成了 DAE 和 RF，带有一个分类单元，用于脑 MRI 的分类。最后，分割的特征由具有 BMOA 和 RF 的 DAE 分级。所提出的方法在MATLAB站点上执行，并用现有方法进行性能分析。