用于现有和迫在眉睫的肺部疾病检测的深度学习范式:综述

Q4 Veterinary
Bhavna Vohra, S. Mittal
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引用次数: 1

摘要

临床医生对哮喘、慢性阻塞性肺病、结核病、癌症等肺部疾病的诊断依赖于通过X射线和MRI等各种手段拍摄的图像。近年来,深度学习(DL)范式放大了医学图像领域的发展。随着DL的发展,医学图像中的肺部疾病可以被有效地识别和分类。例如,DL可以在监督模型中检测肺癌癌症,准确率为99.49%,在非监督模型中准确率为95.3%。深度学习模型可以提取无人值守的特征,这些特征可以毫不费力地组合到DL网络架构中,以更好地对一种或两种肺部疾病进行医学图像检查。在这篇综述文章中,在基本的DL模型下,即监督、半监督和无监督学习下,对有效的技术进行了综述,以在较少的人为干预下,代表DL在肺部疾病检测中的增长。添加了最近的技术来理解范式的转变和未来的研究前景。直到2019年,这三种技术都使用了计算机断层扫描(C.T.)图像数据集,但在疫情期间之后,胸部射线照片(X射线)数据集更常用。X射线有助于经济上早期发现肺部疾病,并通过提供早期治疗来挽救生命。每个DL模型都侧重于识别肺部疾病的一些特征。研究人员可以通过使用X射线图像数据集的标准系统来探索DL,以自动检测更多肺部疾病。无监督DL已从肺部疾病的检测扩展到预测,这是在肺部疾病发生之前找出发病几率的一个关键里程碑。研究人员可以建立更多的预测模型,识别多种肺部疾病的严重程度,以降低死亡率和相关成本。这篇综述文章旨在帮助研究人员探索能够有效识别和预测肺部疾病并提高准确性的深度学习系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review
Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.
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来源期刊
Journal of Experimental Biology and Agricultural Sciences
Journal of Experimental Biology and Agricultural Sciences Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
1.00
自引率
0.00%
发文量
127
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