This article develops a computer aided fully automated method for detecting and classifying the glioma brain magnetic resonance imaging (MRI) using machine learning classification approach. The noise contents in source brain MRI image are detected and removed using ridgelet filter and then the edges in noise removed image are detected using fuzzy logic and then contrast adaptive local histogram equalization is applied on the edge detected brain image for enhancing the edge pixels. The Gabor transformation is applied on the enhanced brain image and the features are computed from this transformed image. The computed features are optimized using feature optimization technique genetic algorithm (GA) and the optimized features are classified using adaptive neurofuzzy inference system (ANFIS) classification method, which classifies the source brain MRI image into either glioma or non-glioma brain image. Finally, fuzzy C means algorithm is applied on the glioma brain image to segment the tumor regions. The segmented tumor regions in glioma brain image is compared with manually tumor segmented brain image in order to evaluate the performance efficiency of the proposed system and the simulation results shows that the proposed works in this article achieves optimum performance with state of the art methods.

中文翻译：

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基于机器学习分类方法的胶质瘤脑肿瘤检测

本文开发了一种计算机辅助全自动方法，用于使用机器学习分类方法检测和分类胶质瘤脑磁共振成像 (MRI)。使用脊波滤波器检测并去除源脑MRI图像中的噪声内容，然后使用模糊逻辑检测去噪图像中的边缘，然后对边缘检测到的脑图像应用对比度自适应局部直方图均衡，以增强边缘像素。Gabor 变换应用于增强的大脑图像，特征是从这个变换后的图像中计算出来的。计算特征使用特征优化技术遗传算法（GA）进行优化，优化后的特征使用自适应神经模糊推理系统（ANFIS）分类方法进行分类，它将源脑 MRI 图像分类为神经胶质瘤或非神经胶质瘤脑图像。最后，将模糊C均值算法应用于胶质瘤脑图像，对肿瘤区域进行分割。将神经胶质瘤脑图像中的分割肿瘤区域与手动肿瘤分割的脑图像进行比较，以评估所提出系统的性能效率，仿真结果表明，本文提出的工作采用最先进的方法实现了最佳性能。