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.