Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

被引:0
作者
Sehgal, Adarsh [1 ,2 ]
Sehgal, Muskan
La, Hung Manh [1 ,2 ]
Bebis, George [2 ]
机构
[1] Adv Robot & Automat ARA Lab, Reno, NV 89557 USA
[2] Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA
来源
ADVANCES IN VISUAL COMPUTING, ISVC 2022, PT II | 2022年 / 13599卷
关键词
Breast mass detection; Genetic algorithm; GA-E2E; COMPUTER-AIDED DETECTION; GENETIC ALGORITHM; UPDATE; PERFORMANCE; ACCURACY;
D O I
10.1007/978-3-031-20716-7_21
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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 breast 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.
引用
收藏
页码:270 / 283
页数:14
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