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Learned and handcrafted features for early-stage laryngeal SCC diagnosis

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Abstract

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.

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.

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Authors and Affiliations

  1. Center for MicroElectroMechanical Systems (CMEMs), Informatics Department, University of Minho, Braga, Portugal

    Tiago Araújo

  2. Center for MicroElectroMechanical Systems (CMEMs), Industrial Electronics Department, University of Minho, Guimarães, Portugal

    Cristina P. Santos

  3. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

    Elena De Momi

  4. Department of Information Engineering, Universitá Politecnica delle Marche, Ancona, Italy

    Sara Moccia

  5. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy

    Sara Moccia

Authors
  1. Tiago Araújo
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  2. Cristina P. Santos
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  3. Elena De Momi
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  4. Sara Moccia
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Correspondence to Tiago Araújo.

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Araújo, T., Santos, C.P., De Momi, E. et al. Learned and handcrafted features for early-stage laryngeal SCC diagnosis. Med Biol Eng Comput 57, 2683–2692 (2019). https://doi.org/10.1007/s11517-019-02051-5

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  • DOI: https://doi.org/10.1007/s11517-019-02051-5

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