The brain tumor detection is a highly complicated but significant task. The early detection of a brain tumor can increase the survival rate of an individual by providing proper treatment. This work proposes a computer‐aided diagnostic method for brain tumor detection using fractional wavelet transform (FrDWT) with different values of alpha (α) ranging from (0.1‐1), histogram‐based various local feature descriptors, feature selectors, and two classification methods, that is, support vector machine (SVM) as well as artificial neural network (ANN). The brain MR images dataset is taken from BraTS 2015, e‐health laboratory, and Harvard Medical School. The FrDWT and the local feature descriptors used are combined to extract features. Some of the features are selected using Eigenvector centrality and Laplacian Score techniques. The selected features are trained and classified by the two classifiers, SVM and ANN. It is a type of binary classification, so the labels provided to the classifier are named “normal” and “abnormal.” The performance is estimated using parameters like accuracy, sensitivity, precision, specificity, and F1 score. The results of FrDWT are compared with conventional discrete wavelet transform (DWT), and it is observed that FrDWT outperforms DWT at alpha (α) values lower than 0.5.

中文翻译：

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基于分数小波变换的磁共振成像脑肿瘤诊断系统

脑肿瘤的检测是一个高度复杂但重要的任务。通过提供适当的治疗方法，早期发现脑瘤可以提高个体的生存率。这项工作提出了一种使用分数小波变换（FrDWT）进行脑肿瘤检测的计算机辅助诊断方法，该分数小波变换（α）的值范围从（0.1-1），基于直方图的各种局部特征描述符，特征选择器和两种分类方法，即支持向量机（SVM）和人工神经网络（ANN）。大脑MR图像数据集来自BraTS 2015，电子保健实验室和哈佛医学院。结合使用FrDWT和使用的局部特征描述符以提取特征。使用特征向量中心性和Laplacian Score技术选择某些特征。所选功能由SVM和ANN这两个分类器进行训练和分类。这是一种二进制分类，因此提供给分类器的标签分别称为“正常”和“异常”。使用诸如准确性，敏感性，准确性，特异性和F1分数之类的参数来评估性能。将FrDWT的结果与常规离散小波变换（DWT）进行比较，可以观察到，在α（α）值低于0.5时，FrDWT优于DWT。