甲状腺癌是一种危及生命的疾病，由位于喉结下方颈部额叶区域的甲状腺细胞引起。虽然它不像其他类型的癌症那样普遍，但它在影响内分泌系统的常见癌症中名列前茅。机器学习已成为一种有价值的医疗诊断工具，专门用于检测甲状腺异常。特征选择在机器学习领域至关重要，因为它有助于降低数据维度并集中于最相关的特征。此过程提高了模型性能、减少了训练时间并增强了可解释性。这项研究检查了用于特征选择的 FOX 优化算法的二进制变体。该研究采用八个传递函数（S 形和 V 形）将 FOX 优化算法转换为其二进制版本。基于视觉变压器的预训练模型（DeiT 和 Swin Transformer）用于特征提取。使用局部线性嵌入对提取的特征进行转换，并结合朴素贝叶斯分类器应用二进制 FOX 优化算法进行特征选择。该研究利用了与甲状腺癌图像相关的两个数据集（超声数据集和组织病理学数据集）。使用基于半二次理论的集成排名技术进行基准测试。采用两种基于 TOPSIS 的方法（H-TOPSIS 和 A-TOPSIS）进行初始模型排名，然后采用集成技术进行最终排名。该问题被视为多目标优化任务，以精度、F2-score、AUC-ROC 和特征空间大小作为优化目标。与使用数据集和特征提取技术的其他变体相比，基于[公式：参见文本]传递函数的二进制 FOX 优化算法实现了卓越的性能。所提出的框架包括用于提取特征的 Swin 变压器、用于特征选择的具有 V1 传递函数的 Fox 优化算法以及朴素贝叶斯分类器，并在两个数据集上获得了最佳性能。最佳模型的准确率达到 94.75%，AUC-ROC 值为 0.9848，F2-Score 为 0.9365，推理时间为 0.0353 秒，并为超声数据集选择了 5 个特征。对于组织病理学数据集，诊断模型的总体准确率为 89.71%，AUC-ROC 得分为 0.9329，F2-Score 为 0.8760，推理时间为 0.05141 秒，并选择了 12 个特征。所提出的模型取得了与现有小特征空间研究相当的结果。© 2023。作者。

Thyroid cancer is a life-threatening condition that arises from the cells of the thyroid gland located in the neck's frontal region just below the adam's apple. While it is not as prevalent as other types of cancer, it ranks prominently among the commonly observed cancers affecting the endocrine system. Machine learning has emerged as a valuable medical diagnostics tool specifically for detecting thyroid abnormalities. Feature selection is of vital importance in the field of machine learning as it serves to decrease the data dimensionality and concentrate on the most pertinent features. This process improves model performance, reduces training time, and enhances interpretability. This study examined binary variants of FOX-optimization algorithms for feature selection. The study employed eight transfer functions (S and V shape) to convert the FOX-optimization algorithms into their binary versions. The vision transformer-based pre-trained models (DeiT and Swin Transformer) are used for feature extraction. The extracted features are transformed using locally linear embedding, and binary FOX-optimization algorithms are applied for feature selection in conjunction with the Naïve Bayes classifier. The study utilized two datasets (ultrasound and histopathological) related to thyroid cancer images. The benchmarking is performed using the half-quadratic theory-based ensemble ranking technique. Two TOPSIS-based methods (H-TOPSIS and A-TOPSIS) are employed for initial model ranking, followed by an ensemble technique for final ranking. The problem is treated as multi-objective optimization task with accuracy, F2-score, AUC-ROC and feature space size as optimization goals. The binary FOX-optimization algorithm based on the [Formula: see text] transfer function achieved superior performance compared to other variants using both datasets as well as feature extraction techniques. The proposed framework comprised a Swin transformer to extract features, a Fox optimization algorithm with a V1 transfer function for feature selection, and a Naïve Bayes classifier and obtained the best performance for both datasets. The best model achieved an accuracy of 94.75%, an AUC-ROC value of 0.9848, an F2-Score of 0.9365, an inference time of 0.0353 seconds, and selected 5 features for the ultrasound dataset. For the histopathological dataset, the diagnosis model achieved an overall accuracy of 89.71%, an AUC-ROC score of 0.9329, an F2-Score of 0.8760, an inference time of 0.05141 seconds, and selected 12 features. The proposed model achieved results comparable to existing research with small features space.© 2023. The Author(s).