# Convolutional capsule network for classification of breast cancer histology images

arXiv: https://arxiv.org/abs/1804.08376 · HTML: https://mlsanity.com/paper/1804.08376/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Parallel Capsule Networks for Classification of White Blood Cells](https://mlsanity.com/paper/2108.02644/) (2021)
- [Capsule network with shortcut routing](https://mlsanity.com/paper/2307.10212/) (2023)
- [Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images](https://mlsanity.com/paper/2309.08745/) (2023)
- [One-Shot Identification with Different Neural Network Approaches](https://mlsanity.com/paper/2601.08278/) (2026)
- [Multiple Additive Neural Networks for Structured and Unstructured Data](https://mlsanity.com/paper/2604.26888/) (2026)

## Abstract (arXiv)

Automatization of the diagnosis of any kind of disease is of great importance and it's gaining speed as more and more deep learning solutions are applied to different problems. One of such computer aided systems could be a decision support too able to accurately differentiate between different types of breast cancer histological images - normal tissue or carcinoma. In this paper authors present a deep learning solution, based on convolutional capsule network for classification of four types of images of breast tissue biopsy when hematoxylin and eusin staining is applied. The cross-validation accuracy was achieved to be 0.87 with equaly high sensitivity.