Learning-based classification of informative laryngoscopic frames.

Journal: Computer methods and programs in biomedicine
PMID: 29544787

Abstract

BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance.

Authors

  • Sara Moccia
    Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy. Electronic address: sara.moccia@iit.it.
  • Gabriele O Vanone
    Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Elena De Momi
    Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Andrea Laborai
    Department of Otorhinolaryngology, Head and Neck Surgery, University of Genoa, Genoa, Italy.
  • Luca Guastini
    Università degli Studi di Genova, Genoa, Italy.
  • Giorgio Peretti
    Università degli Studi di Genova, Genoa, Italy.
  • Leonardo S Mattos

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