A lightweight network for automatic thyroid nodules location and recognition with high speed and accuracy in ultrasound images.
J Xray Sci Technol
; 30(5): 967-981, 2022.
Article
em En
| MEDLINE
| ID: mdl-35661047
ABSTRACT
BACKGROUND:
The intelligent diagnosis of thyroid nodules in ultrasound image is an important research issue. Automatically locating the region of interest (ROI) of thyroid nodules and providing pre-diagnosis results can help doctors to diagnose faster and more accurate.OBJECTIVES:
This study aims to propose a model, which can detect multiple nodules stably and accurately in order to avoid missed detection and misjudgment. In addition, the detection speed of the model needs to be fast for real-time diagnosis in ultrasound images.METHODS:
Based on the object detection technology, we propose an accurate, robust and high-speed network with multiscale fusion strategy called Efficient-YOLO, which can realize the localization and recognition of nodules at the same time. Finally, multiple metrics are used to measure the diagnostic ability of the model.RESULTS:
Experimental results conducted on 3,562 ultrasound images show that our new model greatly increases the accuracy and speed of the detection compared with the baseline model. The best mAP is 92.64%, and the fastest detection speed is 45.1 frames per second.CONCLUSIONS:
This study proposed an effective method to diagnosis thyroid nodules automatically, which can meet the real-time requirements, indicating that its effectiveness and feasibility for future clinical application.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Nódulo da Glândula Tireoide
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Xray Sci Technol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China