Hasan, Ali M. and Al-Waely, Noor K. N. and Aljobouri, Hadeel K. and Jalab, Hamid Abdullah and Ibrahim, Rabha W. and Meziane, Farid (2024) Molecular subtypes classification of breast cancer in DCE-MRI using deep features. Expert Systems with Applications, 236. ISSN 1873-6793, DOI https://doi.org/10.1016/j.eswa.2023.121371.
Full text not available from this repository.Abstract
Breast cancer is a major cause of concern on a global scale due to its high incidence rate. It is one of the leading causes of death for women, if left untreated. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly being used in the evaluation of breast cancer. Prior studies neglected to take into account breast cancer characteristics and features that might be helpful for distinguishing the four molecular subtypes of breast cancer. The use of breast DCE-MRI to identify the molecular subtypes is now the focus of research in breast cancer analysis. It offers breast cancer patients a better chance for an early and effective treatment plan. A manually annotated dataset of 1359 DCE-MRI images was used in this study, with 70% used for training and the remaining for testing. Twelve deep features were extracted from this dataset. The dataset was initially pre-processed through placing the ROIs by a radiologist experienced in breast MRI interpretation, then deep features are extracted using the proposed convolutional neural network (CNN). Finally, the deep features extracted are classified into molecular subtypes of breast cancer using the support vector machine (SVM). The effectiveness of the predictive model was assessed using accuracy and area under curve (AUC) measures. The test was performed on unseen held-out data. The maximum achieved accuracy and AUC were 99.78% and 100% respectively, with substantially a low complexity rate.
| Item Type: | Article |
|---|---|
| Funders: | Al-Nahrain University [CM-PMP-22-001] |
| Uncontrolled Keywords: | Molecular subtypes; Breast cancer; DCE-MRI; Classification; Deep learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 05 Jul 2024 07:41 |
| Last Modified: | 05 Jul 2024 07:41 |
| URI: | http://eprints.um.edu.my/id/eprint/44321 |
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