ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules

Overview

Journal Comput Model Eng Sci

Date 2024 Apr 3

PMID 38566835

Authors

Lu Chen

Huaqiang Chen

Zhikai Pan

Sheng Xu

Guangsheng Lai

Shuwen Chen

Shuihua Wang

Xiaodong Gu

Yudong Zhang

Affiliations

Soon will be listed here.

Abstract

Aim: This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately.

Methods: A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model's generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit a novel deep learning model, ThyroidNet, to accurately detect thyroid nodules.

Results: ThyroidNet was evaluated on private datasets and was comparable to other existing methods, including U-Net and TransUnet. Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules. It achieved improved accuracy of 3.9% and 1.5%, respectively.

Conclusion: ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks. Future research directions include optimization of the model structure, expansion of the dataset size, reduction of computational complexity and memory requirements, and exploration of additional applications of ThyroidNet in medical image analysis.

Citing Articles

Improving diagnostic precision in thyroid nodule segmentation from ultrasound images with a self-attention mechanism-based Swin U-Net model.

Yang C, Ashraf M, Riaz M, Umwanzavugaye P, Chipusu K, Huang H Front Oncol. 2025; 15:1456563.

PMID: 39980538 PMC: 11839427. DOI: 10.3389/fonc.2025.1456563.

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