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[An Improved Object Detection Algorithm for Thyroid Nodule Ultrasound Image Based on Faster R-CNN].
Zheng, Tianlei; Yang, Na; Geng, Shi; Zhao, Xianyun; Wang, Yue; Cheng, Deqiang; Zhao, Lei.
Afiliación
  • Zheng T; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
  • Yang N; Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221004, China.
  • Geng S; Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221004, China.
  • Zhao X; Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221004, China.
  • Wang Y; Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221004, China.
  • Cheng D; Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221004, China.
  • Zhao L; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Sichuan Da Xue Xue Bao Yi Xue Ban ; 54(5): 915-922, 2023 Sep.
Article en Zh | MEDLINE | ID: mdl-37866946
ABSTRACT

Objective:

To propose an improved algorithm for thyroid nodule object detection based on Faster R-CNN so as to improve the detection precision of thyroid nodules in ultrasound images.

Methods:

The algorithm used ResNeSt50 combined with deformable convolution (DC) as the backbone network to improve the detection effect of irregularly shaped nodules. Feature pyramid networks (FPN) and Region of Interest (RoI) Align were introduced in the back of the trunk network. The former was used to reduce missed or mistaken detection of thyroid nodules, and the latter was used to improve the detection precision of small nodules. To improve the generalization ability of the model, parameters were updated during backpropagation with an optimizer improved by Sharpness-Aware Minimization (SAM).

Results:

In this experiment, 6 261 thyroid ultrasound images from the Affiliated Hospital of Xuzhou Medical University and the First Hospital of Nanjing were used to compare and evaluate the effectiveness of the improved algorithm. According to the findings, the algorithm showed optimization effect to a certain degree, with the AP50 of the final test set being as high as 97.4% and AP@50595 also showing a 10.0% improvement compared with the original model. Compared with both the original model and the existing models, the improved algorithm had higher detection precision and improved capacity to detect thyroid nodules with better accuracy and precision. In particular, the improved algorithm had a higher recall rate under the requirement of lower detection frame precision.

Conclusion:

The improved method proposed in the study is an effective object detection algorithm for thyroid nodules and can be used to detect thyroid nodules with accuracy and precision.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Nódulo Tiroideo Límite: Humans Idioma: Zh Revista: Sichuan Da Xue Xue Bao Yi Xue Ban Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Nódulo Tiroideo Límite: Humans Idioma: Zh Revista: Sichuan Da Xue Xue Bao Yi Xue Ban Año: 2023 Tipo del documento: Article País de afiliación: China