Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms
This work addresses hyperparameter tuning for breast cancer detection in mammograms, which is an incremental improvement in a domain-specific application.
The paper tackled hyperparameter optimization for deep learning models in breast mass detection, using a Genetic Algorithm-based approach called GA-E2E, which improved the area under the curve (AUC) for classifier performance.
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.