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Research on thyroid nodule segmentation using an improved U-Net network

Peng Xu1

1 College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China

* Corresponding Author: Peng Xu (email)

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 2024, 40(2), 1-7. https://doi.org/10.23967/j.rimni.2024.05.012

Abstract

To develop a precise neural network model designed for segmenting ultrasound images of thyroid nodules. The deep learning U-Net network model was utilized as the main backbone, with improvements made to the convolutional operations and the implementation of multilayer perceptron modeling at the lower levels, using the more effective BCEDice loss function. The modified network achieved enhanced segmentation precision and robust generalization capabilities, with a Dice coefficient of 0.9062, precision of 0.9153, recall of 0.9023, and an F1 score of 0.9062, indicating improvements over the U-Net and Swin-Unet to various extents. The U-Net network enhancement presented in this study outperforms the original U-Net across all performance indicators. This advancement could help physicians make more precise and efficient diagnoses, thereby minimizing medical errors.

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APA Style
Xu, P. (2024). Research on thyroid nodule segmentation using an improved u-net network. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 40(2), 1-7. https://doi.org/10.23967/j.rimni.2024.05.012
Vancouver Style
Xu P. Research on thyroid nodule segmentation using an improved u-net network. Rev int métodos numér cálc diseño ing. 2024;40(2):1-7 https://doi.org/10.23967/j.rimni.2024.05.012
IEEE Style
P. Xu, “Research on thyroid nodule segmentation using an improved U-Net network,” Rev. int. métodos numér. cálc. diseño ing., vol. 40, no. 2, pp. 1-7, 2024. https://doi.org/10.23967/j.rimni.2024.05.012



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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