Blog

A new interesting article has been published in Med Biol Eng Comput. 2019 Dec; 57(12):2683-2692. doi: 10.1007/s11517-019-02051-5. Epub 2019 Nov 14. and titled:

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

Authors of this article are:

Araújo T, Santos CP, De Momi E, Moccia S.

A summary of the article is shown below:

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 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.

Check out the article’s website on Pubmed for more information:

[link-preview url=https://www.ncbi.nlm.nih.gov/pubmed/31728933 forceshot=true]

This article is a good source of information and a good way to become familiar with topics such as: Convolutional neural networks; Deep learning; Laryngeal cancer; Machine learning; Textural analysis; Tissue classification.

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