Trang Nguyen Thi Hoang, Long Khuong Quynh, An Pham Le, Dang Tran Ngoc
Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Center for Population Health Science and Data Science, Ha Noi University of Public Health, Ha Noi 100000, Vietnam.
Diagnostics (Basel). 2023 Jan 17;13(3):346. doi: 10.3390/diagnostics13030346.
Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning classifiers.
This study was verified using 731 images from 357 women who underwent at least one mammogram and had clinical records for at least six months before mammography. The model was trained on mammograms and clinical variables to discriminate benign and malignant lesions. Multiple pre-trained deep CNN models to detect cancer in mammograms, including X-ception, VGG16, ResNet-v2, ResNet50, and CNN3 were employed. Machine learning models were constructed using k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Artificial Neural Network (ANN), and gradient boosting machine (GBM) in the clinical dataset.
The detection performance obtained an accuracy of 84.5% with a specificity of 78.1% at a sensitivity of 89.7% and an AUC of 0.88. When trained on mammography image data alone, the result achieved a slightly lower score than the combined model (accuracy, 72.5% vs. 84.5%, respectively).
A breast cancer-detection model combining machine learning and deep learning models was performed in this study with a satisfactory result, and this model has potential clinical applications.
基于人工智能（AI）的分析乳腺癌的计算模型已经开发了数十年。本研究旨在使用机器学习和深度学习分类器，研究联合乳腺钼靶图像和临床记录用于乳腺癌检测的准确性和效率。
本研究使用了来自357名女性的731张图像进行验证，这些女性至少进行了一次乳腺钼靶检查，并且在乳腺钼靶检查前至少有六个月的临床记录。该模型在乳腺钼靶图像和临床变量上进行训练，以区分良性和恶性病变。采用了多个预训练的深度卷积神经网络（CNN）模型来检测乳腺钼靶图像中的癌症，包括X-ception、VGG16、ResNet-v2、ResNet50和CNN3。在临床数据集中使用k近邻（KNN）、支持向量机（SVM）、随机森林（RF）、人工神经网络（ANN）和梯度提升机（GBM）构建机器学习模型。
检测性能在灵敏度为89.7%、特异性为78.1%时，准确率达到84.5%，曲线下面积（AUC）为0.88。仅在乳腺钼靶图像数据上进行训练时，结果得分略低于联合模型（准确率分别为72.5%和84.5%）。
本研究中构建了一个结合机器学习和深度学习模型的乳腺癌检测模型，结果令人满意，该模型具有潜在的临床应用价值。
