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Classification of brain neoplasm from multi-modality MRI with the aid of ANFIS classifier
Multidimensional Systems and Signal Processing ( IF 2.5 ) Pub Date : 2021-02-08 , DOI: 10.1007/s11045-021-00761-4
R. Aarthi , K. Helen Prabha

In medical Image processing, the chief objective is to detect Neoplasm effectively. Neoplasm is basically a sort of abnormal excessive cell growth but when it generates a mass, it is referred as tumors. Brain tumor (BT) is a deadly disease and also it is regarded as a common sort of cancer on adults and even on children. Therefore, early recognition of the correct sort of BT is significant for devising a proper treatment chart and envisioning patients' response to the adopted treatment. Human understanding of countless medical images (Abnormal or Normal) may bring misclassification and thereby there is a requisite of the automated recognition system for classifying the BT types. This paper offers an effective framework for classifying the BT from the multi-modality Magnetic Resonance Images (MRI) by employing ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier. Primarily, the input data-set undertakes the process of skull stripping. Subsequently, the resultant skull striped image undergoes preprocessing utilizing AHE (Adaptive Histogram Equalization). Subsequently, the clustering process is done by employing the Modified-Fuzzy C Means (MFCM) clustering algorithm. From the benign and malignant classes, features are extorted, and then the optimized features are attained utilizing the Adaptive Elephant Herd Optimization (AEHO) algorithm. Finally, the different sorts of BT are effectively classified by implementing the ANFIS classifier. The outcomes are examined and contrasted to the other conventional techniques to corroborate that the proposed work classifies the BT in great efficiency.



中文翻译:

借助ANFIS分类器从多模式MRI对脑肿瘤进行分类

在医学图像处理中,主要目标是有效检测肿瘤。肿瘤基本上是一种异常的细胞过度生长,但是当它产生大量肿瘤时,就被称为肿瘤。脑瘤(BT)是一种致命的疾病,也被认为是成年人甚至儿童的常见癌症。因此,尽早识别出正确的BT类型对于设计正确的治疗图和设想患者对所采用治疗的反应具有重要意义。人类对无数医学图像(异常或正常)的理解可能会导致错误分类,因此,有必要使用自动识别系统对BT类型进行分类。本文为利用ANFIS(自适应神经模糊推理系统)分类器从多模态磁共振图像(MRI)进行BT分类提供了有效的框架。首先,输入数据集承担头骨剥离的过程。随后,利用AHE(自适应直方图均衡化)对所得的头骨条纹图像进行预处理。随后,通过使用改进的模糊C均值(MFCM)聚类算法完成聚类过程。从良性和恶性类中提取特征,然后使用自适应象群优化(AEHO)算法获得优化的特征。最后,通过实施ANFIS分类器,可以有效地对不同种类的BT进行分类。

更新日期:2021-02-09
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