Department of Obstetrics and Gynecology, Virgen de la Arrixaca University Clinic Hospital, Murcia, Spain.
Health Sciences PhD program, Universidad Católica de Murcia UCAM, Murcia, Spain.
PLoS One. 2019 Jul 26;14(7):e0219388. doi: 10.1371/journal.pone.0219388. eCollection 2019.
Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images.
We have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM).
According to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy).
ML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images.
卵巢肿瘤是妇科医生最常见的诊断挑战，超声检查已成为评估卵巢病理和术前区分良恶性卵巢肿瘤的主要技术。然而，超声检查高度依赖于检查者，两位不同的专家在检查同一病例时可能存在重要的差异。本研究的目的是评估不同知名的机器学习（ML）系统，以实现对超声图像中卵巢肿瘤的自动分类。
我们使用了一个真实的患者数据库，其输入特征是从 IOTA 肿瘤图像数据库中的 348 个图像中提取的，这些图像与图像的类别标签一起。对于每个患者病例和超声图像，其输入特征是使用在感兴趣区域（ROI）上计算的傅里叶描述符预先提取的。然后，我们考虑了四种 ML 技术来执行分类阶段：K-最近邻（KNN）、线性判别（LD）、支持向量机（SVM）和极限学习机（ELM）。
根据我们的研究结果，KNN 分类器的预测不准确（准确率低于 60%），与局部逼近的大小无关，而基于 LD、SVM 和 ELM 的分类器在这种生物医学分类中具有稳健性（准确率超过 85%）。
机器学习方法可有效地用于开发基于超声图像的卵巢肿瘤计算机辅助诊断系统的分类阶段。这些方法能够提供高准确率的自动分类。未来的工作应旨在通过集成技术来增强分类器的设计。另一项正在进行的工作是利用从超声图像中提取的不同类型的特征。
