Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy

Abstract

The use of high-speed video-endoscopy (HSV) in the study of phonatory processes linked to speech needs the precise identification of vocal fold boundaries at the time of vibration. The HSV is a unique laryngeal imaging technology that captures intracycle vocal fold vibrations at a higher frame rate without the need for auditory inputs. The HSV is also effective in identifying the vibrational characteristics of the vocal folds with an increased temporal resolution during retained phonation and flowing speech. Clinically significant vocal fold vibratory characteristics in running speech can be retrieved by creating automated algorithms for extracting HSV-based vocal fold vibration data. The best deep learning-based diagnosis and categorization of vocal fold abnormalities is due to the usage of HSV (ODL-VFDDC). The suggested ODL-VFDDC technique starts with temporal segmentation and motion correction to identify vocalized regions from the HSV recording and gathers the position of movable vocal folds across frames. The attributes gathered are fed into the deep belief network (DBN) model. Furthermore, the agricultural fertility algorithm (AFA) is used to optimize the hyperparameter tuning of the DBN model, which improves classification results. In terms of vocal fold disorder classification, the testing results demonstrated that the ODL-VFDDC technique beats the other existing methodologies. The farmland fertility algorithm (FFA) is then used to accurately determine the glottal limits of vibrating vocal folds. The suggested method has successfully tracked the speech fold boundaries across frames with minimum processing cost and high resilience to picture noise. This method gives a way to look at how the vocal folds move during a connected speech that is completely done by itself.

Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model

“…20,21 Others use various techniques to analyze glottic dynamics, including VF tracking and glottic segmentation. 22,24,27,31,33,37,38,41 Automated segmentation is used in low-speed videoendoscopy to analyze glottic movement in transnasal endoscopy videos and to detect laryngeal adductor reflex events. 29,40,43,44 Estimation of glottic midline can facilitate assessment of oscillatory symmetry.…”

“…Another, which inspired the methodology of this project, uses DeepLabCut to track VF motion 20,21 . Others use various techniques to analyze glottic dynamics, including VF tracking and glottic segmentation 22,24,27,31,33,37,38,41 . Automated segmentation is used in low‐speed videoendoscopy to analyze glottic movement in transnasal endoscopy videos and to detect laryngeal adductor reflex events 29,40,43,44 .…”

“…Automated techniques have been developed for humans and rodents which increase accuracy and efficiency of VF movement analysis and facilitate quantification of subtle variations 13,17–44 . Anatomical differences between humans and rats can impair adaptation of VF tracking tools developed for humans 45,46 .…”