Omiotek Zbigniew
Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Lublin, Poland.
Proc Inst Mech Eng H. 2017 Aug;231(8):774-782. doi: 10.1177/0954411917702682. Epub 2017 Apr 12.
The purpose of the study was to construct an efficient classifier that, along with a given reduced set of discriminant features, could be used as a part of the computer system in automatic identification and classification of ultrasound images of the thyroid gland, which is aimed to detect cases affected by Hashimoto's thyroiditis. A total of 10 supervised learning techniques and a majority vote for the combined classifier were used. Two models were proposed as a result of the classifier's construction. The first one is based on the K-nearest neighbours method (for K = 7). It uses three discriminant features and affords sensitivity equal to 88.1%, specificity of 66.7% and classification error at a level of 21.8%. The second model is a combined classifier, which was constructed using three-component classifiers. They are based on the K-nearest neighbours method (for K = 7), linear discriminant analysis and a boosting algorithm. The combined classifier is based on 48 discriminant features. It allows to achieve the classification sensitivity equal to 88.1%, specificity of 69.4% and classification error at a level of 20.5%. The combined classifier allows to improve the classification quality compared to the single model. The models, built as a part of the automatic computer system, may support the physician, especially in first-contact hospitals, in diagnosis of cases that are difficult to recognise based on ultrasound images. The high sensitivity of constructed classification models indicates high detection accuracy of the sick cases, and this is beneficial to the patients from a medical point of view.
本研究的目的是构建一个高效的分类器，该分类器与给定的一组精简判别特征一起，可用作计算机系统的一部分，用于甲状腺超声图像的自动识别和分类，旨在检测受桥本甲状腺炎影响的病例。总共使用了10种监督学习技术以及对组合分类器的多数投票。作为分类器构建的结果，提出了两种模型。第一个基于K近邻方法（K = 7）。它使用三个判别特征，灵敏度为88.1%，特异性为66.7%，分类误差为21.8%。第二个模型是一个组合分类器，它由三个组件分类器构建而成。它们基于K近邻方法（K = 7）、线性判别分析和一种提升算法。组合分类器基于48个判别特征。它能够实现分类灵敏度为88.1%，特异性为69.4%，分类误差为20.5%。与单一模型相比，组合分类器能够提高分类质量。作为自动计算机系统一部分构建的这些模型，可以支持医生，尤其是在一级医院，对基于超声图像难以识别的病例进行诊断。所构建分类模型的高灵敏度表明患病病例的检测准确率高，从医学角度来看，这对患者是有益的。
