PDF(Pubmed)
Abstract：
BACKGROUND: Ultrasound-based radiomic features to differentiate between benign and malignant breast lesions with the help of machine learning is currently being researched. The mean echogenicity ratio has been used for the diagnosis of malignant breast lesions. However, gray scale intensity histogram values as a single radiomic feature for the detection of malignant breast lesions using machine learning algorithms have not been explored yet.
OBJECTIVE: This study aims to assess the utility of a simple convolutional neural network in classifying benign and malignant breast lesions using gray scale intensity values of the lesion.
METHODS: An open-access online data set of 200 ultrasonogram breast lesions were collected, and regions of interest were drawn over the lesions. The gray scale intensity values of the lesions were extracted. An input file containing the values and an output file consisting of the breast lesions\' diagnoses were created. The convolutional neural network was trained using the files and tested on the whole data set.
RESULTS: The trained convolutional neural network had an accuracy of 94.5% and a precision of 94%. The sensitivity and specificity were 94.9% and 94.1%, respectively.
CONCLUSIONS: Simple neural networks, which are cheap and easy to use, can be applied to diagnose malignant breast lesions with gray scale intensity values obtained from ultrasonogram images in low-resource settings with minimal personnel.
OBJECTIVE: This study aims to assess the utility of a simple convolutional neural network in classifying benign and malignant breast lesions using gray scale intensity values of the lesion.
METHODS: An open-access online data set of 200 ultrasonogram breast lesions were collected, and regions of interest were drawn over the lesions. The gray scale intensity values of the lesions were extracted. An input file containing the values and an output file consisting of the breast lesions\' diagnoses were created. The convolutional neural network was trained using the files and tested on the whole data set.
RESULTS: The trained convolutional neural network had an accuracy of 94.5% and a precision of 94%. The sensitivity and specificity were 94.9% and 94.1%, respectively.
CONCLUSIONS: Simple neural networks, which are cheap and easy to use, can be applied to diagnose malignant breast lesions with gray scale intensity values obtained from ultrasonogram images in low-resource settings with minimal personnel.
摘要：
背景：目前正在研究基于超声的放射组学特征，以借助机器学习来区分良性和恶性乳腺病变。平均回声比已用于诊断恶性乳腺病变。然而,灰度强度直方图值作为使用机器学习算法检测恶性乳腺病变的单一影像组学特征尚未被探索。
目的：本研究旨在评估简单的卷积神经网络在使用病变的灰度强度值对良性和恶性乳腺病变进行分类中的实用性。
方法：收集200个超声乳腺病变的开放式在线数据集，并在病变上绘制感兴趣的区域。提取病变的灰度强度值。创建包含值的输入文件和由乳腺病变诊断组成的输出文件。使用这些文件对卷积神经网络进行训练，并在整个数据集上进行测试。
结果：经训练的卷积神经网络的准确率为94.5％，精度为94％。敏感性和特异性分别为94.9%和94.1%,分别。
结论：简单的神经网络，便宜且易于使用，可应用于诊断恶性乳腺病变与灰度强度值获得的超声图像在低资源设置与最少的人员。
目的：本研究旨在评估简单的卷积神经网络在使用病变的灰度强度值对良性和恶性乳腺病变进行分类中的实用性。
方法：收集200个超声乳腺病变的开放式在线数据集，并在病变上绘制感兴趣的区域。提取病变的灰度强度值。创建包含值的输入文件和由乳腺病变诊断组成的输出文件。使用这些文件对卷积神经网络进行训练，并在整个数据集上进行测试。
结果：经训练的卷积神经网络的准确率为94.5％，精度为94％。敏感性和特异性分别为94.9%和94.1%,分别。
结论：简单的神经网络，便宜且易于使用，可应用于诊断恶性乳腺病变与灰度强度值获得的超声图像在低资源设置与最少的人员。
