# Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network

arXiv: https://arxiv.org/abs/1811.04241 · HTML: https://mlsanity.com/paper/1811.04241/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2018.

## Built on by (most cited first, 15 of 15)

- [Domain Adaptation under Open Set Label Shift](https://mlsanity.com/paper/2207.13048/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach](https://mlsanity.com/paper/2305.11728/) (2023)
- [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2107.09602/) (2021)
- [A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic Measures](https://mlsanity.com/paper/2102.06997/) (2021)
- [Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis](https://mlsanity.com/paper/2301.01931/) (2023)
- [Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid](https://mlsanity.com/paper/2104.11551/) (2021)
- [Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform](https://mlsanity.com/paper/2202.13270/) (2022)
- [Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification](https://mlsanity.com/paper/2307.12790/) (2023)
- [Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification](https://mlsanity.com/paper/2410.03333/) (2024)
- [BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading](https://mlsanity.com/paper/2107.05037/) (2021)
- [Multiscale Analysis for Improving Texture Classification](https://mlsanity.com/paper/2204.09841/) (2022)
- [Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review](https://mlsanity.com/paper/2210.11943/) (2022)
- [Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images](https://mlsanity.com/paper/2311.07321/) (2023)
- [AdaptoVision: A Multi-Resolution Image Recognition Model for Robust and Scalable Classification](https://mlsanity.com/paper/2504.12652/) (2025)

## Abstract (arXiv)

The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer Classification Challenge 2015. The experimental results are compared against the existing machine learning and deep learning-based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, Area Under the Curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.