Work

Detection of Esophageal Wall via Deep Learning-Based Object Detection with Ultrasound System

Year: 2024

Type: conference-paper

Source: IEEE Region 10 Conference (TENCON)

DOI: 10.1109/tencon61640.2024.10903045

Authors: Rikuto Matsuyama, Yutaka Suzuki, Shuya Shida, Motoki Arakawa

Institution: Toyo University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: closed

Funder: Japan Society for the Promotion of Science

Awards: Measurement Technology for Human Sensibility Information Based on Long-Term Bowel Sound Monitoring, Non-invasive swallowing evaluation by analysis of surface myoelectric activity, swallowing sounds, and cervical ultrasound videos

OpenAlex ID: W4408258675

FWCI:

Cites: 7

Cited by:

Related to: 10