Automated accurate categorization of brain magnetic resonance images (MRI) is very important for disease diagnosis and treatment. In this paper, a new methodology for the detection of abnormality in brain MRI is suggested. The scale-invariant feature transform is first employed to extract features of MRI. Principal component analysis is applied to the extracted features and a minimal set of more essential features is obtained. Lastly, the obtained feature set is categorized as healthy or unhealthy using support vector machine (SVM)-based classification. The parameters of SVM, i.e. $C$ and $\sigma$ are optimized using Gray Wolf Optimization. However external noise and patient/organ movement degrade the quality of MRI, which in turn affect the classification accuracy. Therefore, a hybrid of Savitzky–Golay smoothing filter and speckle reducing anisotropic diffusion filter is used for preprocessing of the source image, which efficiently reduces the noise while preserving edges of the image. It is revealed from the results that proposed technique provides a classification accuracy of 99.61%. Thus the suggested technique may effectively diagnose diseases using MRI.

脑磁共振图像 (MRI) 的自动准确分类对于疾病诊断和治疗非常重要。在本文中，提出了一种检测脑部 MRI 异常的新方法。首先采用尺度不变特征变换来提取 MRI 的特征。将主成分分析应用于提取的特征，并获得更基本特征的最小集合。最后，使用基于支持向量机 (SVM) 的分类将获得的特征集分类为健康或不健康。SVM的参数，即 $C$和$\sigma$使用灰狼优化进行优化。然而，外部噪声和患者/器官运动会降低 MRI 的质量，进而影响分类准确性。因此，Savitzky–Golay 平滑滤波器和散斑减少各向异性扩散滤波器的混合用于源图像的预处理，在保留图像边缘的同时有效地降低了噪声。结果表明，所提出的技术提供了 99.61% 的分类准确率。因此，所建议的技术可以使用 MRI 有效地诊断疾病。