ECE Department, PSG College of Technology, Coimbatore 641004, India.
Comput Methods Programs Biomed. 2014;113(1):202-9. doi: 10.1016/j.cmpb.2013.10.011. Epub 2013 Oct 18.
Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer aided classification method in computed tomography (CT) images of lungs developed using artificial neural network. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The statistical parameters like mean, standard deviation, skewness, kurtosis, fifth central moment and sixth central moment are used for classification. The classification process is done by feed forward and feed forward back propagation neural networks. Compared to feed forward networks the feed forward back propagation network gives better classification. The parameter skewness gives the maximum classification accuracy. Among the already available thirteen training functions of back propagation neural network, the Traingdx function gives the maximum classification accuracy of 91.1%. Two new training functions are proposed in this paper. The results show that the proposed training function 1 gives an accuracy of 93.3%, specificity of 100% and sensitivity of 91.4% and a mean square error of 0.998. The proposed training function 2 gives a classification accuracy of 93.3% and minimum mean square error of 0.0942.
癌症的早期检测是提高患者生存机会的最有前途的方法。本文提出了一种使用人工神经网络开发的肺部计算机辅助分类方法。从 CT 图像中分割整个肺部，并从分割后的图像中计算参数。使用均值、标准差、偏度、峰度、第五中心矩和第六中心矩等统计参数进行分类。分类过程通过前馈和前馈反向传播神经网络完成。与前馈网络相比，前馈反向传播网络提供了更好的分类效果。参数偏度提供了最高的分类准确率。在已经存在的 13 种反向传播神经网络的训练函数中，Traingdx 函数给出了 91.1%的最大分类准确率。本文提出了两种新的训练函数。结果表明，所提出的训练函数 1 的准确率为 93.3%、特异性为 100%、灵敏度为 91.4%，均方误差为 0.998。所提出的训练函数 2 的分类准确率为 93.3%，最小均方误差为 0.0942。
