Enhanced fuzzy Gaussian networks for sputum image based Mycobacterium detection

K. Mithra
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Abstract

ABSTRACT The primary step involved in the diagnosis of Mycobacterium tuberculosis is the Sputum Smear Microscopy. The limitations of manual detection can be avoided with an automated technique which is carried out in this study. This paper uses the Enhanced Fuzzy Gaussian Networks (EFGN) for the detection of TB, by integrating the Gaussian model with the fuzzy and the neural network. In EFGN, the classified output obtained from the enhanced fuzzy and neural network is combined together depending on the Gaussian mixture model. The sputum smear microscopic image acts as the input, to which the process of thresholding is imposed to get the segmented result. Local Gradient Pattern (LGP), length, density, area, and histogram features are utilized to classify and count the bacilli objects using EFGN classifier. The performance is estimated based on the factors, such as Segmentation Accuracy (SA), and Mean Squared Error (MSE).
基于痰液图像的增强模糊高斯网络分枝杆菌检测
诊断结核分枝杆菌的首要步骤是痰涂片镜检。人工检测的局限性可以通过在本研究中进行的自动化技术来避免。本文采用增强模糊高斯网络(EFGN)进行结核病的检测,将高斯模型与模糊神经网络相结合。在EFGN中,根据高斯混合模型,将增强模糊网络和神经网络得到的分类输出组合在一起。以痰涂片显微图像为输入,对其进行阈值分割处理,得到分割结果。利用局部梯度模式(LGP)、长度、密度、面积和直方图特征,利用EFGN分类器对杆菌对象进行分类和计数。性能是基于分割精度(SA)和均方误差(MSE)等因素来估计的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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