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A Thyroid Nodule Classification Method Based on TI-RADS

机译：基于TI-RADS的甲状腺结节分类方法

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摘要

Thyroid Imaging Reporting and Data System(TI-RADS) is a valuable tool for differentiating the benign and the malignant thyroid nodules. In clinic, doctors can determine the extent of being benign or malignant in terms of different classes by using TI-RADS. Classification represents the degree of malignancy of thyroid nodules. TI-RADS as a classification standard can be used to guide the ultrasonic doctor to examine thyroid nodules more accurately and reliably. In this paper, we aim to classify the thyroid nodules with the help of TI-RADS. To this end, four ultrasound signs, i.e., cystic and solid, echo pattern, boundary feature and calcification of thyroid nodules are extracted and converted into feature vectors. Then semi-supervised fuzzy C-means ensemble (SS-FCME) model is applied to obtain the classification results. The experimental results demonstrate that the proposed method can help doctors diagnose the thyroid nodules effectively.
机译：甲状腺影像报告和数据系统（TI-RADS）是区分良性和恶性甲状腺结节的有价值的工具。在临床中，医生可以使用TI-RADS来确定不同类别的良性或恶性程度。分类代表甲状腺结节的恶性程度。 TI-RADS作为分类标准可用于指导超声医生更准确，更可靠地检查甲状腺结节。在本文中，我们旨在借助TI-RADS对甲状腺结节进行分类。为此，提取了四个超声信号，即囊性和实性，回声模式，边界特征和甲状腺结节钙化，并将其转换为特征向量。然后应用半监督模糊C均值集成（SS-FCME）模型获得分类结果。实验结果表明，该方法可以帮助医生有效地诊断甲状腺结节。

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