# Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks

arXiv: https://arxiv.org/abs/1909.11870 · HTML: https://mlsanity.com/paper/1909.11870/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
- [Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2312.07965/) (2023)
- [Evaluating histopathology transfer learning with ChampKit](https://mlsanity.com/paper/2206.06862/) (2022)
- [Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology](https://mlsanity.com/paper/2204.08311/) (2022)
- [ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification](https://mlsanity.com/paper/2211.00749/) (2022)
- [Histopathologic Cancer Detection](https://mlsanity.com/paper/2311.07711/) (2023)
- [Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images](https://mlsanity.com/paper/2112.03455/) (2021)
- [Evaluating Generic Auto-ML Tools for Computational Pathology](https://mlsanity.com/paper/2112.03622/) (2021)
- [Rotaflip: A New CNN Layer for Regularization and Rotational Invariance in Medical Images](https://mlsanity.com/paper/2108.02704/) (2021)
- [Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification](https://mlsanity.com/paper/2310.09981/) (2023)
- [Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster](https://mlsanity.com/paper/2509.02440/) (2025)

## Abstract (arXiv)

Breast cancer is one of the leading causes of death across the world in women. Early diagnosis of this type of cancer is critical for treatment and patient care. Computer-aided detection (CAD) systems using convolutional neural networks (CNN) could assist in the classification of abnormalities. In this study, we proposed an ensemble deep learning-based approach for automatic binary classification of breast histology images. The proposed ensemble model adapts three pre-trained CNNs, namely VGG19, MobileNet, and DenseNet. The ensemble model is used for the feature representation and extraction steps. The extracted features are then fed into a multi-layer perceptron classifier to carry out the classification task. Various pre-processing and CNN tuning techniques such as stain-normalization, data augmentation, hyperparameter tuning, and fine-tuning are used to train the model. The proposed method is validated on four publicly available benchmark datasets, i.e., ICIAR, BreakHis, PatchCamelyon, and Bioimaging. The proposed multi-model ensemble method obtains better predictions than single classifiers and machine learning algorithms with accuracies of 98.13%, 95.00%, 94.64% and 83.10% for BreakHis, ICIAR, PatchCamelyon and Bioimaging datasets, respectively.