Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN) Article Swipe
Maleika Heenaye-Mamode Khan
,
Nazmeen B. Boodoo-Jahangeer
,
Wasiimah Dullull
,
Shaista Nathire
,
Xiaohong Gao
,
G. R. Sinha
,
Kapil Kumar Nagwanshi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0256500
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0256500
The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0256500
- OA Status
- gold
- Cited By
- 119
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3195475698
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3195475698Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0256500Digital Object Identifier
- Title
-
Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)Work title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-08-26Full publication date if available
- Authors
-
Maleika Heenaye-Mamode Khan, Nazmeen B. Boodoo-Jahangeer, Wasiimah Dullull, Shaista Nathire, Xiaohong Gao, G. R. Sinha, Kapil Kumar NagwanshiList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0256500Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1371/journal.pone.0256500Direct OA link when available
- Concepts
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Deep learning, Artificial intelligence, Convolutional neural network, Breast cancer, Computer science, Transfer of learning, Cancer, Artificial neural network, Machine learning, Pattern recognition (psychology), Mammography, Radiology, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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119Total citation count in OpenAlex
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2025: 25, 2024: 32, 2023: 34, 2022: 28Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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