Gutenberg Open Science: Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7275

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DC Field	Value	Language
dc.contributor.author	Thiem, Daniel G. E.	-
dc.contributor.author	Römer, Paul	-
dc.contributor.author	Gielisch, Matthias	-
dc.contributor.author	Al-Nawas, Bilal	-
dc.contributor.author	Schlüter, Martin	-
dc.contributor.author	Plaß, Bastian	-
dc.contributor.author	Kämmerer, Peer W.	-
dc.date.accessioned	2022-07-01T09:07:25Z	-
dc.date.available	2022-07-01T09:07:25Z	-
dc.date.issued	2021	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7289	-
dc.description.abstract	BACKGROUND Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library. METHODS A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN). RESULTS The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each. CONCLUSION Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7275	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Head & face medicine	de
jgu.journal.volume	17	de
jgu.pages.alternative	38	de
jgu.publisher.year	2021	-
jgu.publisher.name	BioMed Central	de
jgu.publisher.place	London	de
jgu.publisher.issn	1746-160X	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1186/s13005-021-00292-0	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	JGU-Publikationen

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