论文标题
部分可观测时空混沌系统的无模型预测
Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms
论文作者
论文摘要
通过乳房X线摄影的准确乳腺癌诊断有可能挽救世界各地数百万生命。深度学习（DL）方法已证明对乳房X线照片中的质量检测非常有效。当前DL模型的进一步改进将进一步提高这些方法的有效性。在这种情况下，关键问题是如何为DL模型选择正确的超参数。在本文中，我们提出了GA-E2E，这是一种使用遗传算法（GAS）来调整Brest癌症检测的DL模型超参数的新方法。我们的发现表明，参数值的差异可以极大地改变曲线下的面积（AUC），该区域用于确定分类器的性能。
Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms. Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick the right hyperparameters for DL models. In this paper, we present GA-E2E, a new approach for tuning the hyperparameters of DL models for brest cancer detection using Genetic Algorithms (GAs). Our findings reveal that differences in parameter values can considerably alter the area under the curve (AUC), which is used to determine a classifier's performance.
