Islam, Md. Tobibul and Hoque, Md Enamul and Ullah, Mohammad and Islam, Md. Toufiqul and Nishu, Nabila Akter and Islam, Md. Rabiul (2024) CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma. Cancer Medicine, 13 (16). pp. 1-14. ISSN 2045-7634. (Published)
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Abstract
Objective: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real-time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine-learning framework that will give more accurate predictions for IDC and metastasis cancer classification. Methods: This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding. Results: It is evident that using machine learning techniques significantly (15%–25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC. Conclusions: The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | computer-aided diagnosis (CAD); Convolutional neural network; Histopathological image; Invasive ductal carcinoma (IDC); Metastasis |
| Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Faculty/Division: | Faculty of Industrial Sciences And Technology Institute of Postgraduate Studies Centre for Advanced Intelligent Materials |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 20 Sep 2024 07:40 |
| Last Modified: | 20 Sep 2024 07:40 |
| URI: | http://umpir.ump.edu.my/id/eprint/42625 |
| Download Statistic: | View Download Statistics |
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