This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.

中文翻译：

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使用CANFIS和U-Net CNN分类的磁共振脑图像中基于模糊逻辑的脑膜瘤肿瘤检测

本文使用基于模糊逻辑的增强和协同自适应神经模糊推理系统以及U-Net卷积神经网络分类方法，开发了脑膜瘤脑肿瘤检测过程的方法。拟议的脑膜瘤肿瘤检测过程包括以下几个阶段：增强，特征提取和分类。使用模糊逻辑对源脑图像进行增强，然后在不同的缩放级别上对这种增强的图像应用对偶树复小波变换。根据分解后的子带图像计算特征，并使用CANFIS分类方法对这些特征进行进一步分类，该方法可从非脑膜瘤脑图像中识别出脑膜瘤脑图像。