Ensemble of Deep Features for Breast Cancer Histopathological Image Classification

• Published in: Computer journal Vol. 67; no. 6; pp. 2126 - 2136
• Main Authors: Atwan, Jaffar, Almansour, Nedaa, Hashem Ryalat, Mohammad, Sahran, Shahnorbanun, Aldabbas, Hamza, Albashish, Dheeb
• Format: Journal Article
• Language: English
• Published: Oxford University Press 24.06.2024
• Subjects: ensemble learning; transfer learning; deep convolutional neural networks; breast cancer classification; deep features extraction
• ISSN: 0010-4620; 1460-2067
• DOI: 10.1093/comjnl/bxad127

Analysis of histopathological images (HIs) is crucial for detecting breast cancer (BR). However, because they vary, it is still very difficult to extract well-designed elements. Deep learning (DL) is a recent development that is used to extract high-level features. However, DL techniques continue to confront several difficult problems, such as the need for sufficient training data for DL models, which reduces the classification findings. In this study, an ensemble deep transfer convolutional neural network is presented to address this problem. The pre-trained models (ResNet50 and MobileNet) are employed to extract high-level features by freezing the front layer parameters while fine-tuning the last layers. In the proposed ensemble framework, KNN, SVM, logistic regression and neural networks are used as base classifiers. The majority vote and product approaches are used to integrate the predictions of each separate classifier. In the benchmark BreaKHis dataset, the suggested ensemble model is compared to some current approaches. It demonstrates that while the ensemble model obtains a considerable accuracy of 97.72% for the multiclass classification test, it achieves an accuracy of 99.2% for the binary task. The suggested ensemble model’s effectiveness in extracting useful features for BR images is demonstrated by comparison with existing cutting-edge models.