# Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay

arXiv: https://arxiv.org/abs/1711.10752 · HTML: https://mlsanity.com/paper/1711.10752/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps](https://mlsanity.com/paper/2103.12155/) (2021)
- [Online unsupervised Learning for domain shift in COVID-19 CT scan datasets](https://mlsanity.com/paper/2108.02002/) (2021)

## Abstract (arXiv)

In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on a deep Convolutional Neural Network (CNN) model, to build an end-to-end learning process that classifies breast mass lesions. We investigate the impact that has transfer learning when large data is scarce, and explore the proper way to fine-tune the layers to learn features that are more specific to the new data. The proposed approach showed better performance compared to other proposals that classified the same dataset.