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Performance analysis of computer aided brain tumor detection system using ANFIS classifier

机译:使用ANFIS分类器的计算机辅助脑肿瘤检测系统的性能分析

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The abrupt changes in brain cells due to the environmental effects or genetic disorders leads to form the abnormal lesions in brain. These abnormal lesions are combined as mass and known as tumor. The detection of these tumor cells in brain image is a complex task due to the similarities between normal cells and tumor cells. In this paper, an automated brain tumor detection and segmentation methodology is proposed. The proposed method consists of feature extraction, classification and segmentation. In this paper, Grey Level Co-Occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Law's texture features are used as features. These features are fed to Adaptive Neuro Fuzzy Inference System (ANFIS) classifier as input pattern, which classifies the brain image. Morphological operations are now applied on the classified abnormal brain image to segment the tumor regions. The proposed system achieves 95.07% of sensitivity, 99.84% of specificity and 99.80% of accuracy for tumor segmentation.
机译:由于环境影响或遗传疾病,脑细胞的突然变化导致在大脑中形成异常病变。这些异常病变合并为肿块,称为肿瘤。由于正常细胞和肿瘤细胞之间的相似性,因此在大脑图像中检测这些肿瘤细胞是一项复杂的任务。本文提出了一种自动的脑肿瘤检测和分割方法。所提出的方法包括特征提取,分类和分割。本文以灰度共生矩阵(GLCM),离散小波变换(DWT)和Law的纹理特征为特征。这些特征作为输入模式被馈送到自适应神经模糊推理系统(ANFIS)分类器,该分类器对大脑图像进行分类。现在将形态学操作应用于分类的异常脑图像以分割肿瘤区域。拟议的系统可实现95.07%的敏感性,99.84%的特异性和99.80%的肿瘤分割准确性。

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