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An Automated Approach for Brain Tumor Identification using ANN Classifier

机译:ANN分类器的脑肿瘤识别自动方法

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Detection of the tumor and separating it from the background MRI image is most important task in brain image analysis. It needs clinical experts to meet the standard level of accuracy. This limitation is overcome by the application of computer aided technology in medical field for tumor identification and segmentation. In this paper we proposed an efficient tumor segmentation model by using Fuzzy-C-Mean (FCM) clustering, multiple feature extraction using Gabor Wavelets and artificial neural network classifier. The proposed system performance is examined with 40 trained images with 60 tested MRI scanned medical dataset. The proposed system performance is examined in term of accuracy with respect to the confusion matrix. From the result section we proved that we meet required system accuracy level upto 85%.
机译:检测肿瘤并将其与背景MRI图像分离是脑图像分析中最重要的任务。它需要临床专家来满足标准的准确性水平。计算机辅助技术在肿瘤识别和分割中的医学领域应用,克服了这种限制。本文通过使用Gabor小波和人工神经网络分类器使用模糊-C平均值(FCM)聚类,提出了一种高效的肿瘤分割模型,多种特征提取。建议的系统性能检查了40个培训的图像,其中60个具有60个测试的MRI扫描的医疗数据集。在相对于混淆矩阵的准确性期间检查所提出的系统性能。从结果部分,我们证明我们符合所需的系统精度级别高达85 %。

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