Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence
Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence
复制标题
DOI：
10.3390/s20071822
复制
发表时间：
2020-04-01
期刊：
影响因子：
3.9
通讯作者：
Park, Kang Ryoung
中科院分区：
文献类型：
--
作者：
Dat Tien Nguyen;Kang, Jin Kyu;Park, Kang Ryoung
已经开发了计算机辅助诊断系统来帮助医生诊断甲状腺结节，以减少主要基于医生经验的传统诊断方法所造成的错误。因此，这种系统的性能在提高诊断任务的质量方面起着重要的作用。虽然已经有关于这个问题的最先进的研究，这是基于手工特征，深特征，或两者的组合，他们的性能仍然有限。为了克服这些问题，我们提出了一种基于超声图像的甲状腺恶性结节的诊断方法，使用人工智能的基础上，在空间和频率域的分析。此外，我们建议使用加权二进制交叉熵损失函数来训练深度卷积神经网络，以减少训练数据中目标类的不平衡训练样本的影响。通过我们的实验与一个流行的开放数据集，即甲状腺数字图像数据库（TDID），我们证实了我们的方法相比，国家的最先进的方法的优越性。
Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.