Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy

The objective differentiation of facets of cellular metabolism is important for several clinical applications, including accurate definition of tumour boundaries and targeted wound debridement. To this end, spectral biomarkers to differentiate live and necrotic/apoptotic cells have been defined usin...

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Veröffentlicht in:	PloS one 2020-09, Vol.15 (9), p.e0238647-e0238647
Hauptverfasser:	Mellors, Ben O. L, Spear, Abigail M, Howle, Christopher R, Curtis, Kelly, Macildowie, Sara, Dehghani, Hamid
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Apoptosis Biological activity Biology and Life Sciences Biomarkers Cell differentiation Cell metabolism Chemical pollution Classification Clustering Computer science Contamination Cytodiagnosis Data processing Datasets Diagnostic software Diagnostic systems Differentiation Engineering and Technology Engineering schools Health aspects Hyperspectral imaging In vitro methods and tests Infrared imaging Infrared spectroscopy Laboratories Learning algorithms Machine learning Medical diagnosis Medical informatics Medicine and Health Sciences Metabolism Methods Physical Sciences Portable equipment Principal components analysis Resampling Research and Analysis Methods Spectra Spectroscopy Spectrum analysis Supervision Trauma Tumors Wound healing
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