IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning
Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various...
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2021
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oai:doaj.org-article:389ff97fe2f94a1c82444d29a67eca222021-11-09T00:03:08ZIoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning2169-353610.1109/ACCESS.2021.3123472https://doaj.org/article/389ff97fe2f94a1c82444d29a67eca222021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9590500/https://doaj.org/toc/2169-3536Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.Shahan Yamin SiddiquiAmir HaiderTaher M. GhazalMuhammad Adnan KhanIftikhar NaseerSagheer AbbasMuhibur RahmanJunaid Ahmad KhanMunir AhmadMohammad Kamrul HasanAfifi Mohammed. AKaramath AteeqIEEEarticleInternet of Medical Thingsbreast cancer predictiondeep learningconvolutional neural networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146478-146491 (2021) |
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DOAJ |
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DOAJ |
| language |
EN |
| topic |
Internet of Medical Things breast cancer prediction deep learning convolutional neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
| spellingShingle |
Internet of Medical Things breast cancer prediction deep learning convolutional neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shahan Yamin Siddiqui Amir Haider Taher M. Ghazal Muhammad Adnan Khan Iftikhar Naseer Sagheer Abbas Muhibur Rahman Junaid Ahmad Khan Munir Ahmad Mohammad Kamrul Hasan Afifi Mohammed. A Karamath Ateeq IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning |
| description |
Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate. |
| format |
article |
| author |
Shahan Yamin Siddiqui Amir Haider Taher M. Ghazal Muhammad Adnan Khan Iftikhar Naseer Sagheer Abbas Muhibur Rahman Junaid Ahmad Khan Munir Ahmad Mohammad Kamrul Hasan Afifi Mohammed. A Karamath Ateeq |
| author_facet |
Shahan Yamin Siddiqui Amir Haider Taher M. Ghazal Muhammad Adnan Khan Iftikhar Naseer Sagheer Abbas Muhibur Rahman Junaid Ahmad Khan Munir Ahmad Mohammad Kamrul Hasan Afifi Mohammed. A Karamath Ateeq |
| author_sort |
Shahan Yamin Siddiqui |
| title |
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning |
| title_short |
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning |
| title_full |
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning |
| title_fullStr |
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning |
| title_full_unstemmed |
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning |
| title_sort |
iomt cloud-based intelligent prediction of breast cancer stages empowered with deep learning |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/389ff97fe2f94a1c82444d29a67eca22 |
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