Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks

TL;DR

This paper presents an ensemble deep learning approach combining three CNNs for accurate binary classification of breast histology images, demonstrating superior performance on multiple benchmark datasets.

Contribution

It introduces a novel ensemble of pre-trained CNNs with feature extraction and MLP classification for breast cancer histology image analysis.

Findings

Achieved high accuracy across four datasets, up to 98.13%.

Ensemble method outperforms individual CNNs and traditional machine learning.

Effective use of pre-processing and hyperparameter tuning enhances results.

Abstract

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.