Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like “(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification”. Initially, the thyroid images as well as its related data are given as input. From the input image, the features such as“ Grey Level Co-occurrence Matrix(GLCM), Grey level Run Length Matrix(GLRM), proposed Local Binary Pattern(LBP), and Local Tetra Patterns (LTrP)” are extracted. Meanwhile, from the input data, the higher-order statistical features like skewness, kurtosis, entropy, as well as moment get retrieved. Consequently, the Linear Discriminant Analysis (LDA) based dimensionality reduction is processed to resolve the problem of “curse of dimensionality”. Finally, the classification is carried out via two phases: Image features are classified using an ensemble classifier that includes Support Vector Machine (SVM)& Neural Network(NN) models. The data features are subjected to Recurrent Neural Network(RNN) based classification, which is optimized by an Adaptive Elephant Herding Algorithm (AEHO) via tuning the optimal weight. At last, the performance of the adopted scheme is compared to the extant models in terms of various measures. Especially, the mean value of the suggested RNN + AEHO model is 4.35%, 3.54%, 6.07%, 3.8%, 1.69%, 2.85%, 2.07%, 2.54%, 0.13%, 0.035%, and 8.53% better than the existing CNN, NB, RF, KNN, Levenberg, RNN + EHO, RNN + FF, RNN + WOA, WF-CS, FU-SLnO and HFBO methods respectively.

超声 (US) 是一种廉价且非侵入性的技术，用于捕获甲状腺和附近组织的图像。甲状腺疾病的分类和检测仍处于起步阶段。本研究旨在提出一种新的甲状腺诊断方法，包括“（i）特征提取、（ii）特征降维和（iii）分类”三个阶段。最初，甲状腺图像及其相关数据作为输入给出。从输入图像中提取“灰度共生矩阵（GLCM）、灰度游程矩阵（GLRM）、提议的局部二值模式（LBP）和局部四元模式（LTrP）”等特征。同时，从输入数据中，检索到高阶统计特征，如偏度、峰度、熵以及矩。最后，处理基于线性判别分析（LDA）的降维以解决“维数诅咒”问题。最后，分类通过两个阶段进行：使用包括支持向量机 (SVM) 和神经网络 (NN) 模型的集成分类器对图像特征进行分类。数据特征经过基于循环神经网络（RNN）的分类，该分类由自适应大象放牧算法（AEHO）通过调整最佳权重进行优化。最后，将所采用方案的性能与现有模型在各种措施方面进行了比较。尤其是建议的RNN + AEHO模型的平均值比现有模型好4.35%、3.54%、6.07%、3.8%、1.69%、2.85%、2.07%、2.54%、0.13%、0.035%和8.53% CNN、NB、RF、KNN、Levenberg、RNN + EHO、RNN + FF、RNN + WOA、