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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...

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Main Authors: Islam, Md. Tobibul, Hoque, Md Enamul, Ullah, Mohammad, Islam, Md. Toufiqul, Nishu, Nabila Akter, Islam, Md. Rabiul
Format: Article
Language:English
Published: Wiley 2024
Subjects:
Q Science (General)
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Online Access:http://umpir.ump.edu.my/id/eprint/42625/1/CNN-based%20deep%20learning%20approach.pdf
http://umpir.ump.edu.my/id/eprint/42625/
https://doi.org/10.1002/cam4.70069
https://doi.org/10.1002/cam4.70069
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spelling my.ump.umpir.426252024-09-20T07:40:16Z 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
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
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
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.
format Article
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
title 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_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_sort cnn-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma
publisher Wiley
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42625/1/CNN-based%20deep%20learning%20approach.pdf
http://umpir.ump.edu.my/id/eprint/42625/
https://doi.org/10.1002/cam4.70069
https://doi.org/10.1002/cam4.70069
_version_ 1822924668451946496
score 13.232432

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