基于线性回归分类器的无特征30种疾病病理性脑检测系统
A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.
作者信息
Chen Yi, Shao Ying, Yan Jie, Yuan Ti-Fei, Qu Yanwen, Lee Elizabeth, Wang Shuihua
机构信息
School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023,. China.
出版信息
CNS Neurol Disord Drug Targets. 2017;16(1):5-10. doi: 10.2174/1871527314666161124115531.
AIM
Alzheimer's disease patients are increasing rapidly every year. Scholars tend to use computer vision methods to develop automatic diagnosis system. (Background) In 2015, Gorji et al. proposed a novel method using pseudo Zernike moment. They tested four classifiers: learning vector quantization neural network, pattern recognition neural network trained by Levenberg-Marquardt, by resilient backpropagation, and by scaled conjugate gradient.
METHOD
This study presents an improved method by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier.
RESULTS
The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%.
CONCLUSION
Our method performs better than Gorji's approach and five other state-of-the-art approaches. Therefore, it can be used to detect Alzheimer's disease.
目的
阿尔茨海默病患者数量每年都在迅速增加。学者们倾向于使用计算机视觉方法来开发自动诊断系统。（背景）2015年，戈尔吉等人提出了一种使用伪泽尼克矩的新方法。他们测试了四种分类器：学习向量量化神经网络、由Levenberg-Marquardt训练的模式识别神经网络、由弹性反向传播训练的模式识别神经网络以及由缩放共轭梯度训练的模式识别神经网络。
方法
本研究提出了一种改进方法，引入了一种相对较新的分类器——线性回归分类。我们的方法从3D脑图像中选择一个轴向切片，并使用最大阶数为15的伪泽尼克矩从每个图像中提取256个特征。最后，将线性回归分类用作分类器。
结果
所提出的方法获得了97.51%的准确率、96.71%的灵敏度和97.73%的特异性。
结论
我们的方法比戈尔吉的方法以及其他五种最先进的方法表现更好。因此，它可用于检测阿尔茨海默病。
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