Brain tumor and stroke are two important causes of death in and around the world. Tumor classification and retrieval system plays a vital role in medical field. Tumor detection, segmentation and MR imaging seizures are a major concern, although it can be a daunting and tedious task for clinical specialists, the accuracy of which depends solely on their experience. In this article, the neuro fuzzy with binary cuckoo search optimization method is proposed for detecting tumors on MR images. The method has four stages. In the first step, raw MR images are pre-processed by the anisotropic filter, and in the second phase, the removal of the skull is classified by type. The third phase involves the functioning of singular value decomposition and principle component analysis. Finally, the NFBCS method is used to detect and classify tumors and the BCS algorithm optimizes the study model for better classification accuracy.

中文翻译：

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基于二元布谷鸟搜索技术的神经模糊脑MR图像肿瘤检测与分类

脑肿瘤和中风是世界各地和世界各地的两个重要死亡原因。肿瘤分类和检索系统在医学领域起着至关重要的作用。肿瘤检测、分割和 MR 成像癫痫发作是一个主要问题，尽管这对于临床专家来说可能是一项艰巨而乏味的任务，其准确性完全取决于他们的经验。在本文中，提出了基于二元布谷鸟搜索优化的神经模糊算法用于检测 MR 图像上的肿瘤。该方法有四个阶段。在第一步中，原始 MR 图像由各向异性滤波器进行预处理，在第二个阶段，颅骨的去除按类型分类。第三阶段涉及奇异值分解和主成分分析的功能。最后，