Application of K-MTh Algorithm for Accurate Lunge Cancer Detection

M. Aouf
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引用次数: 4

Abstract

The primary goal of this paper is how to detect the lung cancer tumor based on the K-means clustering thresholding (K-MTh) segmentation technique, rate of death from lung cancer diseases is increased in the last years so the discovered of lung cancer early can protect a lot of people from died . The technique of image processing is utilized ,in image processing ,there are a lot of steps are done for improving the performance of medical diagnostic machine .The technique which used is considered very important to classify the degree of a tumor by improving the thresholding technique before using the classification methods such as support vector machine (SVM). Actually, we have applied Gabor Gaussian filtration method to improve and denoise the CT-image, then we applied the segmentation method (K-MTh) and SVM. Finally, the system has been achieved accuracy more than have been expected for classification method after applying K-MTh (more than 90%).
k - m - th算法在肺癌精确检测中的应用
本文的主要目标是如何基于k均值聚类阈值(k -m - th)分割技术检测肺癌肿瘤,近年来肺癌疾病的死亡率不断上升,因此早期发现肺癌可以保护许多人免于死亡。利用图像处理技术,在图像处理中有很多步骤要做,以提高医疗诊断机的性能,所使用的技术是非常重要的,在使用支持向量机(SVM)等分类方法之前,通过改进阈值技术对肿瘤的程度进行分类。实际上,我们先采用Gabor高斯滤波方法对ct图像进行改进和去噪,然后应用分割方法(K-MTh)和SVM。最后,应用k - m - th后,系统达到了超过分类方法预期的准确率(90%以上)。
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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