CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma
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 specialis...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42625/1/CNN-based%20deep%20learning%20approach.pdf |
| _version_ | 1825815915644583936 |
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| author | Islam, Md. Tobibul Hoque, Md Enamul Ullah, Mohammad Islam, Md. Toufiqul Nishu, Nabila Akter Islam, Md. Rabiul |
| author_facet | Islam, Md. Tobibul Hoque, Md Enamul Ullah, Mohammad Islam, Md. Toufiqul Nishu, Nabila Akter Islam, Md. Rabiul |
| author_sort | Islam, Md. Tobibul |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-09-25T03:53:03Z |
| format | Article |
| id | UMPir42625 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:53:03Z |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | dspace |
| spelling | UMPir426252024-09-20T07:40:16Z http://umpir.ump.edu.my/id/eprint/42625/ CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma Islam, Md. Tobibul Hoque, Md Enamul Ullah, Mohammad Islam, Md. Toufiqul Nishu, Nabila Akter Islam, Md. Rabiul Q Science (General) RC0254 Neoplasms. Tumors. Oncology (including Cancer) 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. Wiley 2024-08-30 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42625/1/CNN-based%20deep%20learning%20approach.pdf 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) https://doi.org/10.1002/cam4.70069 https://doi.org/10.1002/cam4.70069 |
| spellingShingle | Q Science (General) RC0254 Neoplasms. Tumors. Oncology (including Cancer) Islam, Md. Tobibul Hoque, Md Enamul Ullah, Mohammad Islam, Md. Toufiqul Nishu, Nabila Akter Islam, Md. Rabiul CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma |
| title | CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma |
| title_full | CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma |
| title_fullStr | CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma |
| title_full_unstemmed | CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma |
| title_short | CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma |
| title_sort | cnn based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma |
| topic | Q Science (General) RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| url | http://umpir.ump.edu.my/id/eprint/42625/1/CNN-based%20deep%20learning%20approach.pdf |
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