A Framework for the Two-Class Classification of Pulmonary Tuberculosis using Artificial Intelligence.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Akansha Nayyar, Rahul Shrivastava, Shruti Jain
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引用次数: 0

Abstract

Aim: The study investigates the creation and assessment of Machine Learning (ML) models using different classifiers such as SVM (Support Vector Machine), logistic regression, decision tree, k-nearest neighbour (kNN), and Artificial Neural Network (ANN) for the automated identification of tuberculosis (TB) from chest X-ray(CXR) images.

Background: As a persistent worldwide health concern, TB requires early detection for effective treatment and control of the infection. The differential diagnosis of TB is a challenge, even for experienced radiologists. With the use of automated processing of CXR images which are reasonable and frequently used for TB diagnosis, employing Artificial Intelligence (AI) techniques provides novel possibilities.

Objective: The objective of the study was to identify respiratory disorders, radiologists devote a lot of time reviewing each of the CXR images. As such, they can identify the type of disease using automated methods based on AI algorithms. This work advances the diagnosis of TB via machine learning, which may result in early treatment options and enhanced outcomes for patients.

Method: The disease was classified using distinct parameters like edge, shape, and Gray Level Difference Statistics (GLDS) on splitting of the dataset at 70:30 and 80:20.

Results: It was observed that authors attained 93.5% accuracy using SVM with linear kernel for a 70:30 data split considering hybrid parameters. The comparison was made considering different feature extraction techniques, different dataset splitting, existing work, and another dataset.

Conclusion: The designed model using SVM, decision tree, kNN, ANN, and logistic regression was compared using other state-of-the-art techniques, other datasets, different feature extraction techniques, and different splitting of data. AI has great promise for enhancing tuberculosis detection, which will ultimately lead to an earlier diagnosis and improved disease management.

基于人工智能的肺结核两类分类框架
目的:本研究探讨了机器学习(ML)模型的创建和评估,使用不同的分类器,如支持向量机(SVM)、逻辑回归、决策树、k近邻(kNN)和人工神经网络(ANN),用于从胸部x射线(CXR)图像中自动识别结核病(TB)。背景:作为一个持续存在的世界卫生问题,结核病需要早期发现以有效治疗和控制感染。结核病的鉴别诊断是一项挑战,即使对经验丰富的放射科医生也是如此。随着对合理且经常用于结核病诊断的CXR图像的自动处理的使用,采用人工智能(AI)技术提供了新的可能性。目的:本研究的目的是识别呼吸系统疾病,放射科医生花费大量时间审查每张CXR图像。因此,他们可以使用基于人工智能算法的自动化方法来识别疾病类型。这项工作通过机器学习推进了结核病的诊断,这可能导致患者的早期治疗选择和改善结果。方法:对数据集进行70:30和80:20的分割,采用边缘、形状、灰度差统计(GLDS)等参数对疾病进行分类。结果:在考虑混合参数的70:30数据分割情况下,作者使用线性核支持向量机获得了93.5%的准确率。考虑不同的特征提取技术、不同的数据集分割、现有工作和另一个数据集,进行了比较。结论:使用支持向量机、决策树、kNN、ANN和逻辑回归设计的模型与其他最新技术、其他数据集、不同特征提取技术和不同数据分割进行了比较。人工智能在加强结核病检测方面大有希望,最终将导致早期诊断和改善疾病管理。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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