{"id":15426,"date":"2023-02-01T00:00:00","date_gmt":"2023-07-18T01:55:00","guid":{"rendered":"https:\/\/bvtn.edu.vn\/?post_type=nghien-cuu&p=15426"},"modified":"2023-07-19T14:26:03","modified_gmt":"2023-07-19T07:26:03","slug":"10-1016-j-amjoto-2023-103800","status":"publish","type":"nghien-cuu","link":"https:\/\/bvtn.edu.vn\/en\/nghien-cuu\/10-1016-j-amjoto-2023-103800\/","title":{"rendered":"Support of deep learning to classify vocal fold images in flexible laryngoscopy"},"comment_status":"closed","ping_status":"closed","template":"","meta":{"update-doi":"false","doi":"10.1016\/j.amjoto.2023.103800","first-author":"Bich Anh Tran","authors":"Bich Anh Tran, Thao Thi Phuong Dao, Ho Dang Quy Dung, Ngoc Boi Van, Chanh Cong Ha, Nam Hoang Pham, Tu Cong Huyen Ton Nu Cam Nguyen, Tan-Cong Nguyen, Minh-Khoi Pham, Mai-Khiem Tran, Truong Minh Tran, Minh-Triet Tran","journal-title":"American Journal of Otolaryngology","publisher":"Elsevier BV","published-date":1675209600,"volume":"44","issue":"3","issn":"0196-0709","title":"Support of deep learning to classify vocal fold images in flexible laryngoscopy","affiliations":"","references":"
  1. Jonathan Reid et al., Development of a machine-learning based voice disorder screening tool. American Journal of Otolaryngology<\/em>. 2022; :. doi: 10.1016\/j.amjoto.2021.103327<\/a>\"Google<\/a><\/li>
  2. Won Ki Cho et al., Diagnostic Accuracies of Laryngeal Diseases Using a Convolutional Neural Network\u2010Based Image Classification System. The Laryngoscope<\/em>. 2021; 131 :. doi: 10.1002\/lary.29595<\/a>\"Google<\/a><\/li>
  3. Won Ki Cho et al., Comparison of Convolutional Neural Network Models for Determination of Vocal Fold Normality in Laryngoscopic Images.. Journal of voice : official journal of the Voice Foundation<\/em>. 2020; :. doi: 10.1016\/j.jvoice.2020.08.003<\/a>\"Google<\/a><\/li>
  4. F. Parker et al., Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas. Annals of Otology, Rhinology & Laryngology<\/em>. 2020; 130 :286 - 291. doi: 10.1177\/0003489420950364<\/a>\"Google<\/a><\/li>
  5. Nathaniel Adamian et al., An Open\u2010Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy. The Laryngoscope<\/em>. 2020; 131 :. doi: 10.1002\/lary.28669<\/a>\"Google<\/a><\/li>
  6. Jian-jun Ren et al., Automatic Recognition of Laryngoscopic Images Using a Deep\u2010Learning Technique. The Laryngoscope<\/em>. 2020; 130 :. doi: 10.1002\/lary.28539<\/a>\"Google<\/a><\/li>
  7. M. Fehling et al., Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network. PLoS ONE<\/em>. 2020; 15 :. doi: 10.1371\/journal.pone.0227791<\/a>\"Google<\/a><\/li>
  8. H. Xiong et al., Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine<\/em>. 2019; 48 :92 - 99. doi: 10.1016\/j.ebiom.2019.08.075<\/a>\"Google<\/a><\/li>
  9. M. Laves et al., A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation. International Journal of Computer Assisted Radiology and Surgery<\/em>. 2018; 14 :483-492. doi: 10.1007\/s11548-018-01910-0<\/a>\"Google<\/a><\/li>
  10. M. Sandler et al., MobileNetV2: Inverted Residuals and Linear Bottlenecks. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition<\/em>. 2018; :4510-4520. doi: 10.1109\/CVPR.2018.00474<\/a>\"Google<\/a><\/li>
  11. Ramprasaath R. Selvaraju et al., Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision<\/em>. 2016; 128 :336-359. doi: 10.1007\/s11263-019-01228-7<\/a>\"Google<\/a><\/li>
  12. Fran\u00e7ois Chollet et al., Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)<\/em>. 2016; :1800-1807. doi: 10.1109\/CVPR.2017.195<\/a>\"Google<\/a><\/li>
  13. Gao Huang et al., Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)<\/em>. 2016; :2261-2269. doi: 10.1109\/CVPR.2017.243<\/a>\"Google<\/a><\/li>
  14. Kaiming He et al., Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)<\/em>. 2015; :770-778. doi: 10.1109\/cvpr.2016.90<\/a>\"Google<\/a><\/li>
  15. H. \u0130. T\u00fcrkmen et al., Classification of laryngeal disorders based on shape and vascular defects of vocal folds. Computers in biology and medicine<\/em>. 2015; 62 :76-85. doi: 10.1016\/j.compbiomed.2015.02.001<\/a>\"Google<\/a><\/li>
  16. Christian Szegedy et al., Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)<\/em>. 2014; :1-9. doi: 10.1109\/CVPR.2015.7298594<\/a>\"Google<\/a><\/li>
  17. A. Verikas et al., A kernel-based approach to categorizing laryngeal images. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society<\/em>. 2007; 31 8 :587-94. doi: 10.1016\/J.COMPMEDIMAG.2007.07.003<\/a>\"Google<\/a><\/li>
  18. Antanas Verikas et al., Multiple feature sets based categorization of laryngeal images. Computer methods and programs in biomedicine<\/em>. 2007; 85 3 :257-66. doi: 10.1016\/j.cmpb.2006.11.002<\/a>\"Google<\/a><\/li>
  19. J. Ilgner et al., Colour texture analysis for quantitative laryngoscopy. Acta Oto-Laryngologica<\/em>. 2003; 123 :730 - 734. doi: 10.1080\/00016480310000412<\/a>\"Google<\/a><\/li>
  20. W. Peter et al., The History of Laryngology: A Centennial Celebration. Otolaryngology- Head and Neck Surgery<\/em>. 1996; 114 :345 - 354. doi: 10.1016\/S0194-59989670202-4<\/a>\"Google<\/a><\/li><\/ol>","tldr":"The results show that current deep learning models can classify vocal fold images well and effectively assist physicians in vocal fold identification and classification of normal or abnormal vocal folds.","research-type":"JournalArticle","bibtex":"@Article{Tran2023SupportOD,\n DOI = {10.1016\/j.amjoto.2023.103800}, author = {Bich Anh Tran and Thao Thi Phuong Dao and Ho Dang Quy Dung and Ngoc Boi Van and Chanh Cong Ha and Nam Hoang Pham and TuNam Nguyen and Tan-Cong Nguyen and Minh Pham and Mai-Khiem Tran and T. M. Tran and Minh-Triet Tran},\n booktitle = {American Journal of Otolaryngology},\n journal = {American journal of otolaryngology},\n pages = {\n 103800\n },\n title = {Support of deep learning to classify vocal fold images in flexible laryngoscopy.},\n volume = {44 3},\n year = {2023}\n}\n"},"loai-nghien-cuu":[124],"class_list":["post-15426","nghien-cuu","type-nghien-cuu","status-publish","hentry","loai-nghien-cuu-quoc-te"],"_links":{"self":[{"href":"https:\/\/bvtn.edu.vn\/en\/wp-json\/wp\/v2\/nghien-cuu\/15426","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bvtn.edu.vn\/en\/wp-json\/wp\/v2\/nghien-cuu"}],"about":[{"href":"https:\/\/bvtn.edu.vn\/en\/wp-json\/wp\/v2\/types\/nghien-cuu"}],"replies":[{"embeddable":true,"href":"https:\/\/bvtn.edu.vn\/en\/wp-json\/wp\/v2\/comments?post=15426"}],"version-history":[{"count":0,"href":"https:\/\/bvtn.edu.vn\/en\/wp-json\/wp\/v2\/nghien-cuu\/15426\/revisions"}],"wp:attachment":[{"href":"https:\/\/bvtn.edu.vn\/en\/wp-json\/wp\/v2\/media?parent=15426"}],"wp:term":[{"taxonomy":"loai-nghien-cuu","embeddable":true,"href":"https:\/\/bvtn.edu.vn\/en\/wp-json\/wp\/v2\/loai-nghien-cuu?post=15426"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}