Precise diagnosis of brain tumour by experienced radiologists involves a complex set of processes including magnetic resonance imaging, magnetic resonance spectroscopy (MRS) data and histopathological evaluations. In this study, a new hybrid feature extraction method, called as aiMRS, based on the negative selection algorithm and clonal selection algorithm of artificial immune systems is developed on MRS data for the detection and classification of brain tumours. In the study, differentiation of benign and malignant brain tumours, classification of normal brain tissue and brain tumour, and detection of metastasis and primary brain tumours are performed with high precision using pattern recognition methods based on the proposed aiMRS method. According to the experimental results performed on a large data set created with the MRS data obtained from INTERPRET database, when the proposed feature extraction method applied, classification of normal brain tissue and brain tumours, benign and malignant brain tumours and metastasis and primary brain tumours is achieved with 100, 98.58 and 98.94% accuracy, respectively. These results show that this proposed system can be used as a secondary tool in physicians' decision-making processes for the classification of brain tumours.

中文翻译：

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aiMRS：一种基于人工免疫算法的MRS信号特征提取方法，用于脑肿瘤分类

由经验丰富的放射科医生精确诊断脑肿瘤涉及一系列复杂过程，包括磁共振成像，磁共振波谱（MRS）数据和组织病理学评估。在这项研究中，基于MRS数据的人工免疫系统的阴性选择算法和克隆选择算法，开发了一种新的混合特征提取方法，称为aiMRS，用于脑肿瘤的检测和分类。在这项研究中，使用基于拟议的aiMRS方法的模式识别方法，可以高精度地进行良性和恶性脑肿瘤的分化，正常脑组织和脑肿瘤的分类以及转移和原发性脑肿瘤的检测。根据对从INTERPRET数据库获得的MRS数据创建的大数据集进行的实验结果，当采用建议的特征提取方法时，正常脑组织和脑肿瘤，良性和恶性脑瘤以及转移和原发性脑瘤的分类为分别达到100％，98.58％和98.94％的精度。这些结果表明，该提议的系统可以用作医师决策过程中脑肿瘤分类的辅助工具。