The primary objective of this paper is to develop a methodology for brain tumor segmentation. Nowadays, brain tumor recognition and fragmentation is one among the pivotal procedure in surgical and medication planning arrangements. It is difficult to segment the tumor area from MRI images due to inaccessibility of edge and appropriately visible boundaries. In this paper, a combination of Artificial Neural Network and Fuzzy K-means algorithm has been presented to segment the tumor locale. It contains four phases, (1) Noise evacuation (2) Attribute extraction and selection (3) Classification and (4) Segmentation. Initially, the procured image is denoised utilizing wiener filter, and then the significant GLCM attributes are extricated from the images. Then Deep Learning based classification has been performed to classify the abnormal images from the normal images. Finally, it is processed through the Fuzzy K-Means algorithm to segment the tumor region separately. This proposed segmentation approach has been verified on BRATS dataset and produces the accuracy of 94%, sensitivity of 98% specificity of 99%, Jaccard index of 96%. The overall accuracy of this proposed technique has been improved by 8% when compared with K-Nearest Neighbor methodology.

本文的主要目的是开发一种脑肿瘤分割方法。如今，脑肿瘤的识别和破碎化已成为外科和药物治疗计划中的关键程序之一。由于难以接近边缘和适当可见的边界，很难从MRI图像中分割出肿瘤区域。本文提出了一种结合人工神经网络和模糊K均值算法对肿瘤部位进行分割的方法。它包含四个阶段，（1）噪声疏散（2）属性提取和选择（3）分类和（4）分割。最初，使用维纳滤波器对获得的图像进行去噪，然后从图像中提取重要的GLCM属性。然后已执行基于深度学习的分类，以将正常图像与异常图像进行分类。最后，通过Fuzzy K-Means算法对其进行处理，以分别分割肿瘤区域。该提议的分割方法已在BRATS数据集上得到验证，其准确性为94％，灵敏度为98％，特异性为99％，Jaccard指数为96％。与K-Nearest Neighbor方法相比，该提议技术的整体准确性提高了8％。