Automatic Thyroid Nodule Detection in Ultrasound Imaging With Improved YOLOv5 Neural Network

With the incidence of thyroid cancer increasing dramatically, the burden of sonographic diagnosis lies heavy for radiologists. An automatic computer-aided diagnosis system with both precision and efficiency is in demand. This retrospective study included 191 ultrasound images of 171 patients (85 ben...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.22662-22670
Hauptverfasser:	Yang, Daqing, Xia, Jianfu, Li, Rizeng, Li, Wencai, Liu, Jisheng, Wang, Rongjian, Qu, Dong, You, Jie
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation attention module Bioinformatics Cancer detection Computer-aided diagnosis Datasets Design automation Diagnosis Feature extraction Hospitals Information retrieval label smoothing Labeling Medical diagnostic imaging Medical imaging Modules Neural networks Nodules Object recognition Regularization Smoothing methods Sonogram Task analysis Thyroid Thyroid gland thyroid nodules Training Ultrasonic imaging ultrasound YOLO YOLOv5 network
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