2024 : 6 : 25
maryam Amiri

maryam Amiri

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0002-7411-9552
Education: PhD.
ScopusId: 57146848900
Faculty: Engineering
Phone: 32625522


A Pre-Trained Ensemble Model for Breast Cancer Grade Detection Based on Small Datasets
Breast cancer, Neural network models, Deep learning classifier, Image classification
Journal Iranian Journal of Health Sciences
Researchers Farhang Jaryani ، maryam Amiri


Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers among women. Early detection of the cancer type is essential to help inform subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large datasets and are not developed for small datasets. Although large datasets may lead to more reliable results, their collecting and processing are challenging. Materials and Methods: This paper proposes a new ensemble deep learning model for breast cancer grade detection based on small datasets. Our model uses some basic deep-learning classifiers to grade the breast tumors, including grades I, II, and III. Since none of the previous works focus on the datasets, including breast cancer grades, we have used a new dataset called Databiox to grade the breast cancers in the three grades. Databiox includes histopathological microscopy images from patients with invasive ductal carcinoma (IDC). Results: The performance of the model is evaluated based on the small dataset. We compare the proposed three-layer ensemble classifier with the most common single deep learning classifiers in terms of accuracy and loss. The experimental results show that the proposed model can improve the classification accuracy of the breast cancer grade compared to the other state-of-the-art single classifiers. Conclusion: The ensemble model can be also used for small datasets. In addition, they can improve the accuracy compared to the other models. This achievement is fundamental for the design of classification-based systems in computer-aided diagnosis.