Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN)
Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, M...
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| Veröffentlicht in: | Journal of medical systems Jg. 44; H. 1; S. 30 - 9 |
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| Sprache: | Englisch |
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01.01.2020
Springer Nature B.V |
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| ISSN: | 0148-5598, 1573-689X, 1573-689X |
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| Abstract | Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC. |
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| AbstractList | Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC. Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC.Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC. |
| ArticleNumber | 30 |
| Author | Agnes, S. Akila Peter, J. Dinesh Anitha, J. Pandian, S. Immanuel Alex |
| Author_xml | – sequence: 1 givenname: S. Akila surname: Agnes fullname: Agnes, S. Akila organization: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences – sequence: 2 givenname: J. surname: Anitha fullname: Anitha, J. organization: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences – sequence: 3 givenname: S. Immanuel Alex surname: Pandian fullname: Pandian, S. Immanuel Alex email: immans@karunya.edu organization: Department of Electronic and communication Engineering, Karunya Institute of Technology and Sciences – sequence: 4 givenname: J. Dinesh surname: Peter fullname: Peter, J. Dinesh organization: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31838610$$D View this record in MEDLINE/PubMed |
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| Keywords | Feature learning Breast cancer Deep convolutional neural network Computer-aided detection Image classification |
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| SubjectTerms | Artificial neural networks Breast cancer Breast Neoplasms - pathology Classification Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image classification Image Interpretation, Computer-Assisted - methods Machine Learning Mammography Mammography - methods Medicine Medicine & Public Health Neural networks Neural Networks, Computer Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences |
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| Title | Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN) |
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