Brain Tumor Detection using Novel Kernel Extreme Learning with Deep Belief Network and Compare Prediction Accuracy with Fuzzy C-means Clustering

V. V. Vardhan Reddy, U. S
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Abstract

To identify the brain tumor according to the categorical identification by using the symptoms. Materials and Methods: To identify brain tumor using Kernel Extreme Learning Machine with improved accuracy over Fuzzy C-means clustering. Results: The proposed hybrid Kernel Extreme Learning Machine approach gives accuracy 93.31% which is significantly better in classification when compared to Fuzzy C-means clustering which has less accuracy 80.14%.and level of significance is 0.01 (p<0.05). Conclusion: Identifying brain tumor was achieved significantly better by using Kernel Extreme Learning Machine compared to Fuzzy C-means clustering.
基于深度信念网络的新型核极值学习脑肿瘤检测及与模糊c均值聚类的预测精度比较
根据症状的分类识别来识别脑肿瘤。材料和方法:利用核极限学习机识别脑肿瘤,比模糊c均值聚类具有更高的准确率。结果:所提出的混合核极限学习机方法的分类准确率为93.31%,与准确率为80.14%的模糊c均值聚类相比,准确率明显提高。显著性水平为0.01 (p<0.05)。结论:与模糊c均值聚类相比,核极限学习机识别脑肿瘤的效果明显更好。
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