Ultrasound (US) imaging is the leading diagnostic method for assessing the early-stage thyroid nodule. However, the visual evaluation of nodules can be influenced by the subjectivity of radiologists' interpretations. Computer-aided Diagnostic (CAD) systems can be useful in classifying these nodules according to their benign or malignant nature. The extraction of the characteristics, which relate in the author's case to the US of thyroid nodules, is essential in the differentiation of these nodules. The complex nature of images, however, generates a significant number of features, many of which are either redundant or irrelevant. This study presents a new CAD system that has been developed to categorise thyroid nodules. In this survey, 447 US images of thyroid nodules were retained. These images were used to extract features using statistical features extraction methods. A feature selection method based on the multi objective particle swarm optimisation algorithm was used to choose the most relevant and non-redundant ones. Then, support vector machine (SVM) and random forests (RFs) were applied to classify these nodules. 10-fold cross-validation was used to assess the classification performance metrics. Their proposed CAD has reached a maximum accuracy of 94.28% for SVM; and 96.13% for RF using the contour-based ROI.

中文翻译：

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超声图像中甲状腺结节分类的特征选择多目标优化

超声（US）成像是评估早期甲状腺结节的主要诊断方法。但是，结节的视觉评估可能会受到放射科医生解释的主观性的影响。根据这些结节的良性或恶性性质，计算机辅助诊断（CAD）系统可用于对这些结节进行分类。提取特征（在作者的案例中与美国的甲状腺结节有关）对于这些结节的分化至关重要。但是，图像的复杂性质会产生大量特征，其中许多特征是多余的或不相关的。这项研究提出了一种新的CAD系统，已对甲状腺结节进行了分类。在这项调查中，保留了447个美国甲状腺结节图像。这些图像用于使用统计特征提取方法来提取特征。采用基于多目标粒子群算法的特征选择方法，选择最相关，最不冗余的特征。然后，应用支持向量机（SVM）和随机森林（RF）对这些结核进行分类。10倍交叉验证用于评估分类性能指标。他们提出的CAD支持SVM的最大精度为94.28％；使用基于轮廓的ROI的RF占96.13％。10倍交叉验证用于评估分类性能指标。他们提出的CAD支持SVM的最大精度为94.28％；使用基于轮廓的ROI的RF占96.13％。10倍交叉验证用于评估分类性能指标。他们提出的CAD支持SVM的最大精度为94.28％；使用基于轮廓的ROI的RF占96.13％。