Advanced computing solutions for analysis of laryngeal disorders

Turkmen, H. Irem; Karslıgil, M. Elif

doi:10.1007/s11517-019-02031-9

Cited by 11 publications

(6 citation statements)

References 76 publications

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“…The aggravation of aging has caused an increase in healthcare costs and shortages in human and material resources. In addition, the unbalanced distribution of medical resources worldwide and the lack of advanced medical technology and equipment in underdeveloped areas, make some sudden diseases not treated timely and effectively [ 3 ]. Moreover, some early symptoms are often imperceptible, resulting in the aggravation of the diseases and the delay in the best treatment.…”

Section: Introductionmentioning

confidence: 99%

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang

Qiu

et al. 2023

Computers in Biology and Medicine

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Section: Introductionmentioning

confidence: 99%

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang

Qiu

et al. 2023

Computers in Biology and Medicine

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“…Some measures of the vocal folds in GAT are shown, however, to be better than others 17 . More clinical applications with advanced computer solutions are needed, as suggested by Turkmen and Karsligil 18 . The insufficiency of the rear glottal area is, of course, observed in various circumstances.…”

Section: Introductionmentioning

confidence: 99%

Localization and quantification of glottal gaps on deep learning segmentation of vocal folds

Pedersen¹,

Larsen

Madsen³

et al. 2023

Sci Rep

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The entire glottis has mostly been the focus in the tracking of the vocal folds, both manually and automatically. From a treatment point of view, the various regions of the glottis are of specific interest. The aim of the study was to test if it was possible to supplement an existing convolutional neural network (CNN) with post-network calculations for the localization and quantification of posterior glottal gaps during phonation, usable for vocal fold function analysis of e.g. laryngopharyngeal reflux findings. 30 subjects/videos with insufficient closure in the rear glottal area and 20 normal subjects/videos were selected from our database, recorded with a commercial high-speed video setup (HSV with 4000 frames per second), and segmented with an open-source CNN for validating voice function. We made post-network calculations to localize and quantify the 10% and 50% distance lines from the rear part of the glottis. The results showed a significant difference using the algorithm at the 10% line distance between the two groups of p < 0.0001 and no difference at 50%. These novel results show that it is possible to use post-network calculations on CNNs for the localization and quantification of posterior glottal gaps.

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“…The visual evaluation of endoscopic images such as CE-NBI, is a subjective process causing difficulty for clinicians to recognize malignant lesions [ 3 , 16 , 17 ]. Several computer-based diagnosis approaches were applied to laryngeal endoscopic images to overcome this issue and present complementary information about the state of the larynx for clinicians [ 18 ]. Recent studies included a Deep Convolutional Neural Network (DCNN) using laryngoscopic images for larynx cancer detection [ 19 ], a set of texture-based features and Deep Learning-based descriptors extracted from endoscopic NBI images for laryngeal Squamous Cell Carcinoma (SCC) detection [ 20 ], a set of texture-based and first-order statistical features [ 21 ] plus an ensemble of Convolution Neural Networks (CNN) with texture and frequency domain based features [ 22 ] for larynx cancerous tissue classification using endoscopic NBI images, a set of features combined with supervised Machine Learning techniques for vascular patterns’ assessment in CE-NBI images and laryngeal cancer diagnosis [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy—Narrow Band Imaging

et al. 2021

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Background: Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)—Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. Methods: The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). Results: The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. Conclusion: The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.

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Advanced computing solutions for analysis of laryngeal disorders

Cited by 11 publications

References 76 publications

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Localization and quantification of glottal gaps on deep learning segmentation of vocal folds

Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy—Narrow Band Imaging

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