Department of Dermatology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, China.
Faculty of Engineering and IT, University of Technology, Sydney, Sydney, NSW 2007, Australia.
Oxid Med Cell Longev. 2022 Oct 17;2022:1526540. doi: 10.1155/2022/1526540. eCollection 2022.
For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of clinicians and reduce the workload of doctors, many ultrasonic computer aided diagnostic systems have been proposed. In recent years, the success of deep learning in image classification and segmentation has made more and more scholars realize the potential performance improvement brought by the application of deep learning in ultrasonic computer-aided diagnosis systems. This study is aimed at applying several machine learning algorithms and develop a machine learning method to diagnose subcutaneous cyst. Clinical features are extracted from datasets and images of ultrasonography of 132 patients from Hunan Provincial People's Hospital in China. All datasets are separated into 70% training and 30% testing. Four kinds of machine learning algorithms including decision tree (DT), support vector machine (SVM), -nearest neighbors (KNN), and neural networks (NN) had been approached to determine the best performance. Compared with all the results from each feature, SVM achieved the best performance from 91.7% to 100%. Results show that SVM performed the highest accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide a good reference in further application to clinical practice of ultrasonography of subcutaneous cyst.
几十年来，由于超声成像具有高度的安全性和高效性，因此被广泛应用于各种疾病的检测。然而，阅读超声图像需要多年的经验和培训。为了支持临床医生的诊断并减轻医生的工作量，已经提出了许多超声计算机辅助诊断系统。近年来，深度学习在图像分类和分割方面的成功，使得越来越多的学者意识到将深度学习应用于超声计算机辅助诊断系统中可以带来潜在的性能提升。本研究旨在应用几种机器学习算法并开发一种机器学习方法来诊断皮下囊肿。从中国湖南省人民医院的 132 名患者的超声数据集和图像中提取临床特征。所有数据集分为 70%的训练集和 30%的测试集。我们采用了四种机器学习算法，包括决策树（DT）、支持向量机（SVM）、-最近邻（KNN）和神经网络（NN），以确定最佳性能。与每种特征的所有结果相比，SVM 的表现最佳，准确率从 91.7%到 100%不等。结果表明，SVM 在超声诊断皮下囊肿方面表现出最高的准确性，这为进一步将超声皮下囊肿的临床应用提供了良好的参考。
