Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four tec...

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Veröffentlicht in:	BMC medical imaging Jg. 24; H. 1; S. 298 - 15
Hauptverfasser:	Lumamba, Kapongo D., Wells, Gordon, Naicker, Delon, Naidoo, Threnesan, Steyn, Adrie J. C., Gwetu, Mandlenkosi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 05.11.2024 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Animal models Bacilli CAD Computed tomography Computer aided design Computer graphics Computer vision CT imaging Deep Learning Digitization Graphics software High resolution Histology Human lung tissue Human tissues Humans Image acquisition Image analysis Image processing Image Processing, Computer-Assisted - methods Image resolution Imaging Imaging, Three-Dimensional - methods Immunohistochemistry Information processing Lung - diagnostic imaging Lung - microbiology Lung - pathology Lung diseases Lungs Machine learning Machine vision Medical imaging Medical imaging equipment Medical research Medicine Medicine & Public Health Mycobacterium tuberculosis - isolation & purification Neural networks Object recognition Pathology Pattern recognition Pattern Recognition, Automated - methods Radiology Reviews Scanning devices Soft tissues State-of-the-art reviews Systematic review Tissues Topology Tuberculosis Tuberculosis, Pulmonary - diagnostic imaging Two dimensional analysis Deep learning Human lung tissue Image analysis Tuberculosis
ISSN:	1471-2342, 1471-2342
Online-Zugang:	Volltext
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