Open Access

ARTICLE

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 ()

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

Received 21 May 2024; Accepted 24 May 2024; Issue published 06 June 2024

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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Keywords

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