Detection of Vocal Cord Ulcer Using Advanced 3D ST Volumetric Segmentation Net Architecture

Abstract

Vocal Cord Ulcer (VCU) is a contact ulcer that decreases the musculoskeletal laryngeal tension when speaking. With the advanced technology, including high-decision cameras and computational energy, it appears to be easy to construct. However, identifying laryngeal variation caused by VCU in CT images is still problematic. The paper aims to use image processing techniques to quantify the laryngeal variation caused by VCU, determine, and analyze its severity. The proposed 3D Swin Transforms Volumetric Segmentation Network (STVSNet) reduces the entanglement and improves the segmentation accuracy. Volumetric quantification on Contrast-Enhanced computed tomography (CECT) uses 3D STVSNet to extract shapes feature to evaluate the VCU severity. Evaluation results were 96.20% sensitivity, 97.15% accuracy, and 96.16% specificity. Concomitantly compared different prevail methods show better results for quantitative data. Experimental results show that 3D STVSNet indicates precise segmentation results for detecting VCU in any image type.

KEYWORDS:

• 3D volumetric segmentation
• Image selection
• Triple transformer
• Vocal folds

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Antony Sophia N

N Antony Sophia is an assistant professor of computer science and engineering at Rajalakshmi Institute of Technology, Chennai. She has completed Bachelor of Engineering and Master of Engineering in computer science and engineering from institutions affiliated to Anna University, Chennai. She has published 10 papers in international journals. Her area of interest includes Image processing, medical imaging, data mining, neural networks, data analytics, pattern recognition.

G.Wiselin Jiji

G Wiselin Jiji is a professor of computer science and engineering at Dr Sivanthi Aditanar College of Engineering, Tiruchendur. She has published more than 78 scientific research papers. She is a recipient of 10 national and three state awards. Her long-term research focuses on computer-aided detection (CAD) and measurement (CAM) of lesions in medical images. CAD research aims to discover the fundamental perception processes of human vision in the image-based diagnosis of lesions and develop mathematical/computational models that describe them. Her areas of interest are computer-aided detection and diagnosis of abnormality, using medical images and medical image analyses, such as image enhancement, segmentation, feature extraction, object detection, and pattern recognition. Email: [email protected]