Medical Imaging for the Detection of Tuberculosis Using Chest Radio Graphs *

2019 International Conference on Advances in the Emerging Computing Technologies (AECT) Pub Date : 2020-02-01 DOI:10.1109/AECT47998.2020.9194212

Abnash Zaman, S. Khattak, Zohaib Hassan

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引用次数: 1

Abstract

Tuberculosis (TB) is the second largest death disease after Human Immunodeficiency Virus (HIV). In Pakistan as well as other countries radiologists faced a lot of problems in diagnosis the TB. The population of Pakistan is about 180,000,000 in which mostly peoples are poor. This paper presents our findings regarding this issue. In this work there is a complete description and review of the previous work done by the researchers. First Chest X-ray (CXR’s) images are segmented through random walker segmentation method and then a set of features on the base of intensity is computed. Computed features will help chest X-ray images to be classified on the bases of computed features as infected or healthy using SVM classifier. System performance is measured on dataset which is taken from Indiana University Hospital Network and data set collection is composed of 100 images. System accuracy is calculated through SVM classifier which is 73%. This research concludes with suggestions for further effort to more improve the quality of the system.

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利用胸部x线图检测结核病的医学成像*

结核病(TB)是仅次于人类免疫缺陷病毒(HIV)的第二大致死疾病。在巴基斯坦和其他国家，放射科医生在诊断结核病时面临许多问题。巴基斯坦人口约为1.8亿，其中大多数人都很贫穷。本文介绍了我们在这个问题上的发现。在这项工作中，有一个完整的描述和研究人员以前所做的工作的回顾。首先采用随机步行者分割法对胸部x线图像进行分割，然后在强度的基础上计算出一组特征。计算特征将帮助基于计算特征的胸部x线图像使用SVM分类器分类为感染或健康。系统的性能是在来自印第安纳大学医院网络的数据集上测量的，数据集由100张图像组成。通过支持向量机分类器计算系统准确率，准确率为73%。本研究最后提出了进一步提高系统质量的建议。

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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来源期刊

2019 International Conference on Advances in the Emerging Computing Technologies (AECT)

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