Chen Hua, Liu Chong, Cheng Xiaoshi, Jiang Chenjun, Wang Ying
School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China.
The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
J Imaging Inform Med. 2025 Mar 10. doi: 10.1007/s10278-024-01120-y.
In recent years, there has been increasing research on computer-aided diagnosis (CAD) using deep learning and image processing techniques. Still, most studies have focused on the benign-malignant classification of nodules. In this study, we propose an integrated architecture for grading thyroid nodules based on the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS). The method combines traditional handcrafted features with deep features in the extraction process. In the preprocessing stage, a pseudo-artifact removal algorithm based on the fast marching method (FMM) is employed, followed by a hybrid median filtering for noise reduction. Contrast-limited adaptive histogram equalization is used for contrast enhancement to restore and enhance the information in ultrasound images. In the feature extraction stage, the improved ShuffleNetV2 network with multi-head self-attention mechanism is selected, and its extracted features are fused with medical prior knowledge features. Finally, a multi-class classification task is performed using the eXtreme Gradient Boosting (XGBoost) classifier. The dataset used in this study consists of 922 original images, including 149 examples belonging to class 2, 140 examples to class 3, 156 examples to class 4A, 114 examples to class 4B, 123 examples to class 4C, and 240 examples to class 5. The model is trained for 2000 epochs. The accuracy, precision, recall, F1 score, and AUC value of the proposed method are 97.17%, 97.65%, 97.17%, 0.9834, and 0.9855, respectively. The results demonstrate that the fusion of medical prior knowledge based on C-TIRADS and deep features from convolutional neural networks can effectively improve the overall performance of thyroid nodule diagnosis, providing a new feasible solution for developing clinical CAD systems for thyroid nodule ultrasound diagnosis.
近年来，利用深度学习和图像处理技术进行计算机辅助诊断（CAD）的研究越来越多。然而，大多数研究都集中在结节的良恶性分类上。在本研究中，我们基于中国甲状腺影像报告和数据系统（C-TIRADS）提出了一种用于甲状腺结节分级的集成架构。该方法在提取过程中将传统手工特征与深度特征相结合。在预处理阶段，采用基于快速行进法（FMM）的伪伪影去除算法，然后进行混合中值滤波以降低噪声。使用对比度受限自适应直方图均衡化进行对比度增强，以恢复和增强超声图像中的信息。在特征提取阶段，选择具有多头自注意力机制的改进型ShuffleNetV2网络，并将其提取的特征与医学先验知识特征进行融合。最后，使用极端梯度提升（XGBoost）分类器执行多类分类任务。本研究中使用的数据集由922张原始图像组成，其中包括属于2类的149个示例、属于3类的140个示例、属于4A类的156个示例、属于4B类的114个示例、属于4C类的123个示例和属于5类的240个示例。该模型训练2000个轮次。所提方法的准确率、精确率、召回率、F1分数和AUC值分别为97.17%、97.65%、97.17%、0.9834和0.9855。结果表明，基于C-TIRADS的医学先验知识与卷积神经网络的深度特征相融合，可以有效提高甲状腺结节诊断的整体性能，为开发甲状腺结节超声诊断的临床CAD系统提供了一种新的可行解决方案。
