Some multiple sclerosis (MS) lesions may have great similarities with neoplastic brain lesions in magnetic resonance (MR) imaging and thus wrong diagnoses may occur. In this study, differentiation of MS and low-grade brain tumors was performed with computer-aided diagnosis (CAD) methods by magnetic resonance spectroscopy (MRS) data. MRS data belonging to 51 MS and 39 low-grade brain tumor patients were obtained. The feature extraction from MRS data was performed by the help of peak integration (PI) and full spectra (FS) methods and the most significant features were identified. For the classification step, artificial neural network (ANN), support vector machine (SVM), and linear discriminant analysis (LDA) methods were used and the differentiation between MS and brain tumor was performed automatically. Examining the results, one can conclude that data which belong to MS and low-grade brain tumor cases were automatically differentiated from each other with the help of ANN with 100% accuracy, 100% sensitivity, and 100% specificity. Using of MR spectroscopy and artificial intelligence methods may be useful as a complementary imaging technique to MR imaging in the differentiation of MS lesions and low-grade brain tumors.

一些多发性硬化症 (MS) 病变可能与磁共振 (MR) 成像中的肿瘤性脑病变有很大的相似性，因此可能会出现错误的诊断。在这项研究中，通过磁共振波谱 (MRS) 数据使用计算机辅助诊断 (CAD) 方法对 MS 和低级别脑肿瘤进行了区分。获得了属于 51 名 MS 和 39 名低级别脑肿瘤患者的 MRS 数据。借助峰积分 (PI) 和全光谱 (FS) 方法从 MRS 数据中提取特征，并确定了最重要的特征。对于分类步骤，使用人工神经网络 (ANN)、支持向量机 (SVM) 和线性判别分析 (LDA) 方法，并自动执行 MS 和脑肿瘤之间的区分。检查结果，可以得出结论，属于 MS 和低级别脑肿瘤病例的数据在 ANN 的帮助下以 100% 的准确度、100% 的灵敏度和 100% 的特异性自动相互区分。磁共振波谱和人工智能方法的使用可作为磁共振成像的补充成像技术，用于区分 MS 病变和低级别脑肿瘤。