Medical image processing and classification are important in medicine. Many Magnetic Resonance Images (MRI) are taken for an individual. To reduce the radiologist workload and to enable more efficiency in brain tumor detection and classification. Many Computer Aided Diagnose (CAD) systems have been developed using different segmentation methods and classification algorithms. This study synthesizes and discusses some studies and their results. A Learning Vector Quantization (LVQ) classifier is used to classify MRI images into normal and abnormal. An initial experiment consisting of normal and abnormal MRI Brain Tumor dataset from UKM Medical Center, to observe various versions of LVQ classifiers performance is conducted.From the extensive and informative studies and numerical experiments, it is expected to obtain better brain tumor classification in the future using Multi pass LVQ classifier which obtained the least standard deviation value (0.4) and the mean accuracy rate is equal to 91%.
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机译：医学图像处理和分类在医学中很重要。一个人拍摄了许多磁共振图像（MRI）。为了减少放射科医生的工作量，并提高脑肿瘤检测和分类的效率。已经使用不同的分割方法和分类算法开发了许多计算机辅助诊断（CAD）系统。本研究综合并讨论了一些研究及其结果。学习向量量化（LVQ）分类器用于将MRI图像分类为正常和异常。进行了由UKM Medical Center的正常和异常MRI脑肿瘤数据集组成的初始实验，以观察各种版本的LVQ分类器的性能，通过广泛而翔实的研究和数值实验，有望在未来获得更好的脑肿瘤分类使用多遍LVQ分类器，该分类器获得了最小的标准偏差值（0.4），平均准确率等于91％。
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