# DenseNet for Breast Tumor Classification in Mammographic Images

arXiv: https://arxiv.org/abs/2101.09637 · HTML: https://mlsanity.com/paper/2101.09637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review](https://mlsanity.com/paper/2010.00961/) (2020)
- [3D Contouring for Breast Tumor in Sonography](https://mlsanity.com/paper/1901.09407/) (2019)
- [MRI Images, Brain Lesions and Deep Learning](https://mlsanity.com/paper/2101.05091/) (2021)

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

- [Breast Cancer Classification with Enhanced Interpretability: DALAResNet50 and DT Grad-CAM](https://mlsanity.com/paper/2308.13150/) (2023)

## Abstract (arXiv)

Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of screening for women are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload by pathologists, who are prone to diagnostic errors. Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to features selection and extraction; and the classification was carried out by DenseNet architecture. Finally, the precision and accuracy of the model is evaluated by cross validation matrix and AUC curve. To summarize, the findings of this study may provide a helpful to improve the diagnosis and efficiency in the automatic tumor localization through the medical image classification.