Breast cancer is one of the most threatening diseases in women's life; thus, the early and accurate diagnosis plays a key role in reducing the risk of death in a patient's life. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social, and cultural issues. Latest advances in computational tools, infrared cameras and devices for bio-impedance quantification, have given a chance to emerge other reference techniques like thermography, infrared thermography, electrical impedance tomography and biomarkers found in blood tests, therefore being faster, reliable and cheaper than other methods. In the last two decades, the techniques mentioned above have been considered as parallel and extended approaches for breast cancer diagnosis, as well many authors concluded that false positives and false negatives rates are significantly reduced. Moreover, when a screening method works together with a computational technique, it generates a "computer-aided diagnosis" system. The present work aims to review the last breakthroughs about the three techniques mentioned earlier, suggested machine learning techniques to breast cancer diagnosis, thus, describing the benefits of some methods in relation with other ones, such as, logistic regression, decision trees, random forest, deep and convolutional neural networks. With this, we studied several hyperparameters optimization approaches with parzen tree optimizers to improve the performance of baseline models. An exploratory data analysis for each database and a benchmark of convolutional neural networks for the database of thermal images are presented. The benchmark process, reviews image classification techniques with convolutional neural networks, like, Resnet50, NasNetmobile, InceptionResnet and Xception.

翻译：乳腺癌是女性生命中最具威胁性的疾病之一，因此早期准确诊断在降低患者死亡风险中起着关键作用。乳腺X线摄影是乳腺癌筛查的参考技术，但由于经济、社会和文化因素，许多国家仍无法获得乳腺X线检查。计算工具、红外相机及生物阻抗量化设备的最新进展，为其他参考技术（如热成像、红外热成像、电阻抗断层成像及血液检测中的生物标志物）的兴起提供了契机，这些技术相比其他方法更快速、可靠且成本更低。在过去二十年中，上述技术已被视为乳腺癌诊断的并行和扩展方法，许多作者得出结论认为，假阳性和假阴性率显著降低。此外，当筛查方法与计算技术结合时，会生成“计算机辅助诊断”系统。本研究旨在综述前述三种技术的最新突破，提出适用于乳腺癌诊断的机器学习技术，从而描述逻辑回归、决策树、随机森林、深度神经网络及卷积神经网络等方法相较于其他方法的优势。基于此，我们研究了多种使用Parzen树优化器的超参数优化方法，以提升基线模型的性能。针对每个数据库进行了探索性数据分析，并针对热图像数据库展示了卷积神经网络的基准测试。该基准测试过程综述了使用卷积神经网络的图像分类技术，如ResNet50、NasNetMobile、InceptionResNet和Xception。