Brain tumor is an uncontrolled mass of tissues in the brain which originate due to mutated growth of tissues. Brain tumor has become a leading cost of death in modern day environment and researchers are inclined to find ways to mitigate the proliferation of this disease. A lot of methods have been applied in brain tumor detection ranging from image processing to signal based analysis. In this study a robust image processing based method is applied using MRI images. MRI images are preferred due to their simplicity and low noise presence. In this study first a clustering based method is used to segment the image and then SVM is applied for tumor detection. A total seven features were considered and were analyzed by the classifiers. SVM with 94.6% accuracy gave a robust result.

中文翻译：

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利用K均值聚类和DWT的MRI图像进行脑肿瘤检测和分类的有效方法

脑瘤是大脑中不受控制的组织块，其起源于组织的突变生长。在现代环境中，脑肿瘤已成为导致死亡的主要成本，研究人员倾向于寻找减轻这种疾病扩散的方法。从图像处理到基于信号的分析，许多方法已应用于脑肿瘤检测。在这项研究中，使用基于MRI图像的基于鲁棒图像处理的方法。MRI图像由于其简单性和低噪声的存在而被首选。在这项研究中，首先使用基于聚类的方法对图像进行分割，然后将SVM用于肿瘤检测。分类器考虑并分析了总共七个特征。精度为94.6％的SVM提供了可靠的结果。