不同人工神经网络对脑肿瘤磁共振图像分类的比较

Yawar Rehman, C. F. Azim
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引用次数: 7

摘要

人工神经网络算法已经被测试用于模式分类,其中最好的是通过二维磁共振图像实现世界卫生组织标准规定的脑肿瘤分类应用。Rajasekaran和Pai (sBAM)技术被发现给出了最成功的将肿瘤分类到正确类别的结果。与其他算法相比,sBAM的计算时间也更短。sBAM技术之前没有在脑肿瘤MR图像上进行过测试,但当它接受测试时,它提供了显著的结果。与同类机构相比,sBAM的成功率也相对较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Different Artificial Neural Networks for Brain Tumour Classification via Magnetic Resonance Images
Artificial Neural Network algorithms has been tested for the classification of patterns and best among them was implemented for the application of brain tumour classification as specified by World Health Organization standards via 2D MR images. The technique of Rajasekaran and Pai (sBAM) was found to give most successful results of classifying tumour into their correct classes. The computation time taken by sBAM was also less as compared with other algorithms. sBAM technique wasn't tested on brain tumour MR images before but when it is subjected to test, it provided prominent results. The success rate of sBAM was also relatively high with its counterparts.
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