Sánchez-Cauce Raquel, Pérez-Martín Jorge, Luque Manuel
Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal, 16, 28040 Madrid, Spain.
Comput Methods Programs Biomed. 2021 Jun;204:106045. doi: 10.1016/j.cmpb.2021.106045. Epub 2021 Mar 16.
Breast cancer is the most common cancer in women. While mammography is the most widely used screening technique for the early detection of this disease, it has several disadvantages such as radiation exposure or high economic cost. Recently, multiple authors studied the ability of machine learning algorithms for early diagnosis of breast cancer using thermal images, showing that thermography can be considered as a complementary test to mammography, or even as a primary test under certain circumstances. Moreover, although some personal and clinical data are considered risk factors of breast cancer, none of these works considered that information jointly with thermal images.
We propose a novel approach for early detection of breast cancer combining thermal images of different views with personal and clinical data, building a multi-input classification model which exploits the benefits of convolutional neural networks for image analysis. First, we searched for structures using only thermal images. Next, we added the clinical data as a new branch of each of these structures, aiming to improve its performance.
We applied our method to the most widely used public database of breast thermal images, the Database for Mastology Research with Infrared Image. The best model achieves a 97% accuracy and an area under the ROC curve of 0.99, with a specificity of 100% and a sensitivity of 83%.
After studying the impact of thermal images and personal and clinical data on multi-input convolutional neural networks for breast cancer diagnosis, we conclude that: (1) adding the lateral views to the front view improves the performance of the classification model, and (2) including personal and clinical data helps the model to recognize sick patients.
乳腺癌是女性中最常见的癌症。虽然乳腺钼靶摄影是用于早期检测该疾病的最广泛使用的筛查技术，但它有几个缺点，如辐射暴露或高经济成本。最近，多位作者研究了使用热成像的机器学习算法对乳腺癌进行早期诊断的能力，表明热成像可被视为乳腺钼靶摄影的补充检查，甚至在某些情况下可作为主要检查。此外，尽管一些个人和临床数据被认为是乳腺癌的危险因素，但这些研究均未将这些信息与热成像结合起来考虑。
我们提出了一种结合不同视角的热成像与个人和临床数据进行乳腺癌早期检测的新方法，构建了一个利用卷积神经网络进行图像分析优势的多输入分类模型。首先，我们仅使用热成像来搜索结构。接下来，我们将临床数据作为这些结构中每一个的新分支添加进去，旨在提高其性能。
我们将我们的方法应用于最广泛使用的乳腺热成像公共数据库——红外图像乳腺病研究数据库。最佳模型实现了97%的准确率和0.99的ROC曲线下面积，特异性为100%，灵敏度为83%。
在研究了热成像以及个人和临床数据对用于乳腺癌诊断的多输入卷积神经网络的影响后，我们得出以下结论：（1）将侧视图添加到正视图可提高分类模型的性能，（2）纳入个人和临床数据有助于模型识别患病患者。
