# Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

arXiv: https://arxiv.org/abs/2207.11244 · HTML: https://mlsanity.com/paper/2207.11244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1905.04100/) (2019)
- [jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics](https://mlsanity.com/paper/1903.02915/) (2019)
- [Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1903.02046/) (2019)

## Built on by (most cited first, 1 of 1)

- [AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay](https://mlsanity.com/paper/2210.12892/) (2022)

## Abstract (arXiv)

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.