Thyroid is a widespread disease, affecting most victims. The diagnosis of thyroid remains a complex process, as its detection in patients is highly intricate. Hence, the doctors are needed to be aware of the risk factors and symptoms of the disease. This paper aims to propose a novel thyroid diagnosis scheme, involving three major phases: (a) feature extraction, (b) optimal feature selection, and (c) classification. Initially, the thyroid image and the related data serve as input for diagnosing the disease. In the first phase, the features like, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), local binary pattern (LBP), local vector pattern (LVP), and local tetra patterns (LTrP) are extracted from the input image. Additionally, the features from data are extracted using Principal Component Analysis (PCA) for resolving the issue of “curse of dimensionality.” The optimal features are then selected using a hybrid optimization approach. The optimally selected features of the image and the data are then subjected to the classification process via convolutional neural network (CNN) and neural network (NN), respectively. Both the classified outputs undergo “AND” binary operation to yield the final classified output. To yield effective classification, the NN model is trained by tuning its weights using the proposed algorithm. Further, this paper introduces a new hybrid algorithm, termed firefly updated lion optimization (SLnO) algorithm (FU-SLnO), for attaining optimal outcomes. Finally, the efficiency of the proposed work is compared over few other conventional approaches and its superiority is proven.

中文翻译：

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用于甲状腺检测的具有元启发式最佳特征选择的混合分类

甲状腺是一种广泛存在的疾病，影响大多数受害者。甲状腺的诊断仍然是一个复杂的过程，因为它在患者中的检测非常复杂。因此，医生需要了解疾病的危险因素和症状。本文旨在提出一种新的甲状腺诊断方案，涉及三个主要阶段：（a）特征提取，（b）最优特征选择，和（c）分类。最初，甲状腺图像和相关数据用作诊断疾病的输入。在第一阶段，灰度共生矩阵（GLCM）、灰度运行长度矩阵（GLRM）、局部二值模式（LBP）、局部向量模式（LVP）和局部四重模式（LTrP）等特征是从输入图像中提取。此外，使用主成分分析（PCA）从数据中提取特征，以解决“维数灾难”的问题。然后使用混合优化方法选择最佳特征。然后分别通过卷积神经网络 (CNN) 和神经网络 (NN) 对图像和数据的最佳选择特征进行分类处理。两个分类输出都经过“与”二元运算以产生最终分类输出。为了产生有效的分类，NN 模型通过使用所提出的算法调整其权重来训练。此外，本文介绍了一种新的混合算法，称为萤火虫更新狮子优化 (SLnO) 算法 (FU-SLnO)，以获得最佳结果。最后，