Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India.
Computer Science and Engineering, SNS College of Technology, Coimbatore, India.
Technol Health Care. 2024;32(6):4381-4402. doi: 10.3233/THC-240362.
Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established.
We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used. Even though binary classification is a common goal of current disease diagnosis techniques, the data sets are small, and the outcomes are not validated. Most current approaches concentrate on model optimization, focusing less on feature engineering.
The suggested method can predict increased protein binding, non-thyroid syndrome (NTIS) (simultaneous non-thyroid disease), autoimmune thyroiditis (compensated hypothyroidism), and Hashimoto's thyroiditis (primary hypothyroidism). In this paper, we develop an automatic thyroid nodule classification system using a multi-scale vision transformer and image enhancement. Graph equalization is the chosen technique for image enhancement, and in our experiments, we used neural networks with four-layer network nodes. This work presents an enhanced linguistic coverage neuro-fuzzy classifier with chosen features for thyroid disease feature selection diagnosis. The training procedure is optimized, and a multi-scale vision transformer network is employed. Each hop connection in Dense Net now has trainable weight parameters, altering the architecture. Images of thyroid nodules from 508 patients make up the data set for this article. Sets of 80% training and 20% validation and 70% training and 30% validation are created from the data. Simultaneously, we take into account how the number of training iterations, network structure, activation function of network nodes, and other factors affect the classification outcomes.
According to the experimental results, the best number of training iterations is 500, the logistic function is the best activation function, and the ideal network structure is 2500-40-2-1.
K-fold validation and performance comparison with previous research validate the suggested methodology's enhanced effectiveness.
涵盖了对母亲早期妊娠甲状腺功能的评估。使用特定负荷的参考范围的好处已得到充分证实。
我们想知道，如果使用早期妊娠多次采集的血样，对母亲甲状腺功能的分类是否会发生变化。尽管二分类是当前疾病诊断技术的常见目标，但数据集较小，且结果未经验证。大多数当前方法侧重于模型优化，较少关注特征工程。
所提出的方法可以预测甲状腺结合蛋白增加、非甲状腺综合征（NTIS）（同时发生的非甲状腺疾病）、自身免疫性甲状腺炎（代偿性甲状腺功能减退）和桥本甲状腺炎（原发性甲状腺功能减退）。在本文中，我们使用多尺度视觉转换器和图像增强开发了一种自动甲状腺结节分类系统。图像增强采用的是灰度均衡化技术，在我们的实验中，使用了具有四个网络节点的神经网络。这项工作提出了一种具有选定特征的增强型语言覆盖神经模糊分类器，用于甲状腺疾病特征选择诊断。优化了训练过程，并使用了多尺度视觉转换器网络。现在，Dense Net 中的每个跳跃连接都具有可训练的权重参数，从而改变了架构。本文的数据来自 508 名患者的甲状腺结节图像。从数据中创建了 80%的训练集和 20%的验证集，以及 70%的训练集和 30%的验证集。同时，我们考虑了训练迭代次数、网络结构、网络节点激活函数等因素对分类结果的影响。
根据实验结果，最佳的训练迭代次数是 500，逻辑函数是最佳的激活函数，理想的网络结构是 2500-40-2-1。
K 折验证和与先前研究的性能比较验证了所提出方法的增强效果。
