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Pre-cancerous stomach lesions detections with Multispectral-augmented endoscopic prototype

Abstract : Featured Application: The presented work has been developed to be used in operating rooms, 1 during upper gastrointestinal exploration of the stomach. The system can distinguish between 2 healthy mucosa, chronic gastritis and intestinal metaplasia. 3 Abstract: In this paper, we are interested in the in vivo detection of pre-cancerous stomach lesions. 4 Pre-cancerous lesions are unfortunately rarely explored in research papers as most of them are 5 focused on cancer detection or conducted ex-vivo. To this purpose, a novel prototype is introduced. It 6 consists of a standard endoscope with multispectral cameras, an optical setup, a fiberscope, and an 7 external light source. Reflectance spectra are acquired in vivo on 16 patients with a healthy stomach, 8 chronic gastritis, or intestinal metaplasia. A specific pipeline has been designed for the classification 9 of spectra between healthy mucosa and different pathologies. The pipeline includes a wavelength 10 clustering algorithm, spectral features computation, and the training of a classifier in a "leave one 11 patient out" manner. Good classification results, around 80% have been obtained, and two attractive 12 wavelength ranges were found in the red and near-infrared ranges: [745, 755 nm] and [780, 840 nm]. 13 The new prototype and the associated results give good arguments in favor of future common use 14 in operating rooms, during upper gastrointestinal exploration of the stomach for the detection of 15 stomach diseases. 16
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02468198
Contributeur : Alexandre Krebs <>
Soumis le : mercredi 5 février 2020 - 15:44:37
Dernière modification le : jeudi 4 juin 2020 - 16:04:02
Archivage à long terme le : : mercredi 6 mai 2020 - 17:42:46

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Alexandre Krebs, Yannick Benezeth, Thomas Bazin, Franck Marzani, Dominique Lamarque. Pre-cancerous stomach lesions detections with Multispectral-augmented endoscopic prototype. Applied Sciences, MDPI, 2020, 10 (3), pp.795. ⟨10.3390/app10030795⟩. ⟨hal-02468198⟩

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