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Learned and handcrafted features for early-stage laryngeal SCC diagnosis.

Squamous cell carcinoma (SCC) is the most common and malignant laryngeal cancer. An early-stage diagnosis is of crucial importance to lower patient mortality and preserve both the laryngeal anatomy and vocal-fold function. However, this may be challenging as the initial larynx modifications, mainly...

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Published in:Medical & Biological Engineering & Computing Vol. 57; no. 12; pp. 2683 - 2693
Main Authors: Araújo, Tiago, Santos, Cristina P., De Momi, Elena, Moccia, Sara
Format: Journal Article
Published: Springer Nature Dec2019
Online Access:View this record in EBSCOhost
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      dt: Dec2019
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11517-019-02051-5
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        atl: Learned and handcrafted features for early-stage laryngeal SCC diagnosis.
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        au:
          Araújo, Tiago
          Santos, Cristina P.
          De Momi, Elena
          Moccia, Sara
        affil: Center for MicroElectroMechanical Systems (CMEMs), Informatics Department, University of Minho, Braga, Portugal
      sug:
        subj:
          Laryngeal Neoplasms Diagnosis
          Vocal Cords Pathology
          Early Detection of Cancer Methods
          Diagnosis, Differential
          Larynx Pathology
          Epithelium Pathology
          Psychological Tests
          Scales
      ab: Squamous cell carcinoma (SCC) is the most common and malignant laryngeal cancer. An early-stage diagnosis is of crucial importance to lower patient mortality and preserve both the laryngeal anatomy and vocal-fold function. However, this may be challenging as the initial larynx modifications, mainly concerning the mucosa vascular tree and the epithelium texture and color, are small and can pass unnoticed to the human eye. The primary goal of this paper was to investigate a learning-based approach to early-stage SCC diagnosis, and compare the use of (i) texture-based global descriptors, such as local binary patterns, and (ii) deep-learning-based descriptors. These features, extracted from endoscopic narrow-band images of the larynx, were classified with support vector machines as to discriminate healthy, precancerous, and early-stage SCC tissues. When tested on a benchmark dataset, a median classification recall of 98% was obtained with the best feature combination, outperforming the state of the art (recall = 95%). Despite further investigation is needed (e.g., testing on a larger dataset), the achieved results support the use of the developed methodology in the actual clinical practice to provide accurate early-stage SCC diagnosis. Graphical Abstract Workflow of the proposed solution. Patches of laryngeal tissue are pre-processed and feature extraction is performed. These features are used in the laryngeal tissue classification.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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