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Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employe...

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Autores principales: Alyami, Jaber, Sadad, Tariq, Rehman, Amjad, Almutairi, Fahad, Saba, Tanzila, Bahaj, Saeed Ali, Alkhurim, Alhassan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452941/
https://www.ncbi.nlm.nih.gov/pubmed/36093488
http://dx.doi.org/10.1155/2022/7403302
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author Alyami, Jaber
Sadad, Tariq
Rehman, Amjad
Almutairi, Fahad
Saba, Tanzila
Bahaj, Saeed Ali
Alkhurim, Alhassan
author_facet Alyami, Jaber
Sadad, Tariq
Rehman, Amjad
Almutairi, Fahad
Saba, Tanzila
Bahaj, Saeed Ali
Alkhurim, Alhassan
author_sort Alyami, Jaber
collection PubMed
description Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.
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spelling pubmed-94529412022-09-09 Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM) Alyami, Jaber Sadad, Tariq Rehman, Amjad Almutairi, Fahad Saba, Tanzila Bahaj, Saeed Ali Alkhurim, Alhassan Comput Intell Neurosci Research Article Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy. Hindawi 2022-08-31 /pmc/articles/PMC9452941/ /pubmed/36093488 http://dx.doi.org/10.1155/2022/7403302 Text en Copyright © 2022 Jaber Alyami et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Alyami, Jaber
Sadad, Tariq
Rehman, Amjad
Almutairi, Fahad
Saba, Tanzila
Bahaj, Saeed Ali
Alkhurim, Alhassan
Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)
title Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)
title_full Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)
title_fullStr Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)
title_full_unstemmed Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)
title_short Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)
title_sort cloud computing-based framework for breast tumor image classification using fusion of alexnet and glcm texture features with ensemble multi-kernel support vector machine (mk-svm)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452941/
https://www.ncbi.nlm.nih.gov/pubmed/36093488
http://dx.doi.org/10.1155/2022/7403302
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