Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network Article Swipe
V. Ulagamuthalvi
,
G. Kulanthaivel
,
A. Balasundaram
,
Arun Kumar Sivaraman
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/csse.2022.023737
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/csse.2022.023737
One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters most. Most of the input images are downscaled, where it is impossible to fetch all the hidden details to reach accuracy in classification. Whereas deep learning algorithms are high efficiency, fully automatic, have more learning capability using more hidden layers, fetch as much as possible hidden information from the input images, and provide an accurate prediction. Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images. The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/csse.2022.023737
- https://file.techscience.com/ueditor/files/csse/TSP_CSSE-43-1/TSP_CSSE_23737/TSP_CSSE_23737.pdf
- OA Status
- diamond
- Cited By
- 8
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293192777
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4293192777Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/csse.2022.023737Digital Object Identifier
- Title
-
Breast Mammogram Analysis and Classification Using Deep Convolution Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
V. Ulagamuthalvi, G. Kulanthaivel, A. Balasundaram, Arun Kumar SivaramanList of authors in order
- Landing page
-
https://doi.org/10.32604/csse.2022.023737Publisher landing page
- PDF URL
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https://file.techscience.com/ueditor/files/csse/TSP_CSSE-43-1/TSP_CSSE_23737/TSP_CSSE_23737.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/ueditor/files/csse/TSP_CSSE-43-1/TSP_CSSE_23737/TSP_CSSE_23737.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer science, Deep learning, Machine learning, Artificial neural network, Convolution (computer science), Convolutional neural network, Breast cancer, Pattern recognition (psychology), Feature extraction, Cancer, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 3, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fully | 51, 130 |
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| abstract_inverted_index.cancer | 21, 45, 170 |
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| abstract_inverted_index.hidden | 116, 138, 145 |
| abstract_inverted_index.highly | 14, 62 |
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| abstract_inverted_index.images | 105 |
| abstract_inverted_index.neural | 165 |
| abstract_inverted_index.stage. | 25 |
| abstract_inverted_index.AlexNet | 160 |
| abstract_inverted_index.Whereas | 123 |
| abstract_inverted_index.cancer. | 11 |
| abstract_inverted_index.details | 117 |
| abstract_inverted_index.disease | 4 |
| abstract_inverted_index.earlier | 24 |
| abstract_inverted_index.focused | 74 |
| abstract_inverted_index.images, | 150 |
| abstract_inverted_index.images. | 71, 88, 173 |
| abstract_inverted_index.layers, | 139 |
| abstract_inverted_index.machine | 34, 80 |
| abstract_inverted_index.matters | 99 |
| abstract_inverted_index.medical | 70, 87 |
| abstract_inverted_index.methods | 73, 82 |
| abstract_inverted_index.natural | 68, 85 |
| abstract_inverted_index.network | 166, 180 |
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| abstract_inverted_index.various | 77 |
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| abstract_inverted_index.accurate | 154 |
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| abstract_inverted_index.existing | 189 |
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| abstract_inverted_index.classifying | 57, 168 |
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| abstract_inverted_index.conventional | 78 |
| abstract_inverted_index.fast-growing | 3 |
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| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.82060126 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |