Brain tumours have increased rapidly in recent years as in other tumour types. Therefore, early and accurate diagnosis of brain tumour is vital for treatment. Magnetic resonance imaging (MRI) and histopathological assessments are the most common methods used in the detection of brain tumours. The research studies on non-invasive imaging methods such as MRI and magnetic resonance spectroscopy (MRS) have become widespread in recent years for brain tumour detection. In this study, a computer-assisted method is proposed for automatic grading of brain tumours on MRS signals. The classification of brain tumours with different grades is performed using long short term memory (LSTM) neural networks. In addition, additional features from MRS signals based on spectral entropy and instantaneous frequency are extracted. As a result of the experimental studies on the international MRS database (INTERPRET), it is seen that grading is achieved using the proposed method with average accuracy of 98.20%, sensitivity of 100%, and specificity of 97.53% performance results in three test studies carried out for the classification of brain tumour. Furthermore, in the grading of brain tumours using the proposed method, the average area under of the receiver operating characteristic curve is measured with high performance of 0.9936.

中文翻译：

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使用LSTM神经网络对磁共振波谱信号进行脑肿瘤自动分级

近年来，脑肿瘤与其他类型的肿瘤一样迅速增长。因此，早期准确诊断脑肿瘤对于治疗至关重要。磁共振成像（MRI）和组织病理学评估是检测脑肿瘤最常用的方法。近年来，对于诸如MRI和磁共振波谱法（MRS）的非侵入性成像方法的研究已经广泛用于脑肿瘤检测。在这项研究中，提出了一种计算机辅助方法，用于根据MRS信号对脑肿瘤进行自动分级。使用长期短期记忆（LSTM）神经网络对不同等级的脑肿瘤进行分类。另外，基于频谱熵和瞬时频率从MRS信号中提取其他特征。作为对国际MRS数据库（INTERPRET）的实验研究的结果，可以看出，使用该方法可实现分级，在三个测试研究中，平均准确度为98.20％，灵敏度为100％，特异性为97.53％。进行脑肿瘤的分类。此外，在使用所提出的方法对脑肿瘤进行分级中，可以以0.9936的高性能测量接收器工作特性曲线的平均面积。