Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.
There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for screening cardiopulmonary abnormalities. In particular, we have observed a strong interest in screening for tuberculosis (TB). This interest has coinci...
| Publicado en: | Journal of Medical Systems Vol. 46; no. 11; pp. 1 - 20 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159925831&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159925831 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2022 vid: 46 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159925831 159925831 159925831 10.1007/s10916-022-01870-8 159925831 ppf: 1 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review. aug: au: Santosh, KC Allu, Siva Rajaraman, Sivaramakrishnan Antani, Sameer affil: Applied Artificial Intelligence (2AI) Research Lab Computer Science Department, University of South Dakota, 57069, Vermillion, SD, USA sug: subj: Tuberculosis Diagnosis Health Screening Deep Learning Radiography, Thoracic Methods Human Systematic Review Meta Analysis Descriptive Statistics Data Analysis Software PubMed Neural Networks (Computer) Algorithms ab: There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for screening cardiopulmonary abnormalities. In particular, we have observed a strong interest in screening for tuberculosis (TB). This interest has coincided with the spectacular advances in deep learning (DL) that is primarily based on convolutional neural networks (CNNs). These advances have resulted in significant research contributions in DL techniques for TB screening using CXR images. We review the research studies published over the last five years (2016-2021). We identify data collections, methodical contributions, and highlight promising methods and challenges. Further, we discuss and compare studies and identify those that offer extension beyond binary decisions for TB, such as region-of-interest localization. In total, we systematically review 54 peer-reviewed research articles and perform meta-analysis. pubtype: Academic Journal doctype: diagnostic images meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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