The high mortality rate associated with brain tumors requires early detection in the early stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis of the brain and tumor tissues is very time‐consuming and operator dependent. Furthermore, there is a need for experts who can review the images to detect these effects, rendering traditional methods inefficient in their presence. Therefore, the use of automated procedures for the careful examination of tumors can prove useful. In this study, a new metaheuristic‐based system is presented for the early detection of brain tumors. The proposed method implements three main steps, namely tumor segmentation, feature extraction, and classification based on a deep belief network. An improved version of the seagull optimization algorithm is adopted for optimal selection of the features and classification of the images. The simulation results of the proposed method are compared with a few existing methods. The final results demonstrate that the proposed method exhibits superior performance in terms of the CDR, FAR, and FRR indices compared with the other methods.

中文翻译：

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基于元启发式和深度学习的脑肿瘤诊断

与脑肿瘤相关的高死亡率要求在早期阶段及早发现以治疗和降低死亡率。由于脑组织的复杂性，对脑和肿瘤组织的手动诊断非常耗时且取决于操作员。此外，需要专家能够审查图像以检测这些影响，从而使传统方法在其存在下效率低下。因此，使用自动化程序仔细检查肿瘤可以证明是有用的。在这项研究中，提出了一种基于元启发式的新系统，用于脑瘤的早期检测。该方法实现了三个主要步骤，即基于深度信念网络的肿瘤分割，特征提取和分类。采用海鸥优化算法的改进版本来对特征进行最佳选择和对图像进行分类。将该方法的仿真结果与现有的几种方法进行了比较。最终结果表明，与其他方法相比，所提出的方法在CDR，FAR和FRR指数方面表现出优异的性能。