We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- 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.
- Authors
Thiem, Daniel G. E.; Römer, Paul; Gielisch, Matthias; Al-Nawas, Bilal; Schlüter, Martin; Plaß, Bastian; Kämmerer, Peer W.
- 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.
- Subjects
ARTIFICIAL intelligence; DEEP learning; COMPUTER vision; ORAL mucosa; FAT; MUCOUS membranes
- Publication
Head & Face Medicine, 2021, Vol 17, Issue 1, p1
- ISSN
1746-160X
- Publication type
Article
- DOI
10.1186/s13005-021-00292-0