Improving Brain Tumor Segmentation with Data Augmentation Strategies Article Swipe
Radhika Malhotra
,
Jasleen Saini
,
Barjinder Singh Saini
,
Savita Gupta
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21467/proceedings.114.2
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21467/proceedings.114.2
In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data in case of supervised learning processes. This is needed to tackle overfitting issues which is a major concern in supervised techniques. Overfitting refers to the phenomenon when a network starts learning specific patterns of the input such that it fits well on the training data but leads to poor generalization abilities on unseen data. The accessibility of enormous quantity of data limits the field of medical domain research. This paper focuses on utility of data augmentation (DA) techniques, which is a well-recognized solution to the problem of limited data. The experiments were performed on the Brain Tumor Segmentation (BraTS) dataset which is available online. The results signify that different DA approaches have upgraded the accuracies for segmenting brain tumor boundaries using CNN based model.
Related Topics
Concepts
Overfitting
Computer science
Artificial intelligence
Convolutional neural network
Segmentation
Machine learning
Market segmentation
Generalization
Deep learning
Field (mathematics)
Domain (mathematical analysis)
Artificial neural network
Pattern recognition (psychology)
Marketing
Mathematics
Business
Mathematical analysis
Pure mathematics
Metadata
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- article
- Language
- en
- Landing Page
- https://doi.org/10.21467/proceedings.114.2
- OA Status
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https://openalex.org/W3204868647Canonical identifier for this work in OpenAlex
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https://doi.org/10.21467/proceedings.114.2Digital Object Identifier
- Title
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Improving Brain Tumor Segmentation with Data Augmentation StrategiesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
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2021-09-22Full publication date if available
- Authors
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Radhika Malhotra, Jasleen Saini, Barjinder Singh Saini, Savita GuptaList of authors in order
- Landing page
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https://doi.org/10.21467/proceedings.114.2Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.21467/proceedings.114.2Direct OA link when available
- Concepts
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Overfitting, Computer science, Artificial intelligence, Convolutional neural network, Segmentation, Machine learning, Market segmentation, Generalization, Deep learning, Field (mathematics), Domain (mathematical analysis), Artificial neural network, Pattern recognition (psychology), Marketing, Mathematics, Business, Mathematical analysis, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.tumor | 168 |
| abstract_inverted_index.using | 170 |
| abstract_inverted_index.which | 63, 128, 150 |
| abstract_inverted_index.domain | 116 |
| abstract_inverted_index.highly | 45 |
| abstract_inverted_index.issues | 62 |
| abstract_inverted_index.limits | 111 |
| abstract_inverted_index.model. | 173 |
| abstract_inverted_index.models | 28 |
| abstract_inverted_index.needed | 58 |
| abstract_inverted_index.neural | 12 |
| abstract_inverted_index.refers | 72 |
| abstract_inverted_index.starts | 79 |
| abstract_inverted_index.tackle | 60 |
| abstract_inverted_index.unseen | 102 |
| abstract_inverted_index.(BraTS) | 148 |
| abstract_inverted_index.concern | 67 |
| abstract_inverted_index.dataset | 149 |
| abstract_inverted_index.decade, | 3 |
| abstract_inverted_index.focuses | 120 |
| abstract_inverted_index.limited | 137 |
| abstract_inverted_index.medical | 115 |
| abstract_inverted_index.network | 78 |
| abstract_inverted_index.online. | 153 |
| abstract_inverted_index.problem | 135 |
| abstract_inverted_index.results | 155 |
| abstract_inverted_index.signify | 156 |
| abstract_inverted_index.utility | 122 |
| abstract_inverted_index.various | 35 |
| abstract_inverted_index.enormous | 107 |
| abstract_inverted_index.learning | 54, 80 |
| abstract_inverted_index.networks | 13, 43 |
| abstract_inverted_index.patterns | 82 |
| abstract_inverted_index.quantity | 108 |
| abstract_inverted_index.solution | 132 |
| abstract_inverted_index.specific | 81 |
| abstract_inverted_index.training | 93 |
| abstract_inverted_index.upgraded | 162 |
| abstract_inverted_index.abilities | 100 |
| abstract_inverted_index.ailments. | 36 |
| abstract_inverted_index.available | 152 |
| abstract_inverted_index.dependent | 46 |
| abstract_inverted_index.detection | 31 |
| abstract_inverted_index.different | 158 |
| abstract_inverted_index.evolution | 9 |
| abstract_inverted_index.performed | 142 |
| abstract_inverted_index.prognosis | 33 |
| abstract_inverted_index.research. | 117 |
| abstract_inverted_index.accuracies | 164 |
| abstract_inverted_index.approaches | 160 |
| abstract_inverted_index.biomedical | 16 |
| abstract_inverted_index.boundaries | 169 |
| abstract_inverted_index.inculcated | 22 |
| abstract_inverted_index.phenomenon | 75 |
| abstract_inverted_index.processes. | 55 |
| abstract_inverted_index.remarkable | 8 |
| abstract_inverted_index.segmenting | 166 |
| abstract_inverted_index.supervised | 53, 69 |
| abstract_inverted_index.Overfitting | 71 |
| abstract_inverted_index.experiments | 140 |
| abstract_inverted_index.overfitting | 61 |
| abstract_inverted_index.processing. | 18 |
| abstract_inverted_index.techniques, | 127 |
| abstract_inverted_index.techniques. | 70 |
| abstract_inverted_index.Segmentation | 147 |
| abstract_inverted_index.augmentation | 125 |
| abstract_inverted_index.improvements | 20 |
| abstract_inverted_index.accessibility | 105 |
| abstract_inverted_index.convolutional | 11 |
| abstract_inverted_index.computer-aided | 30 |
| abstract_inverted_index.generalization | 99 |
| abstract_inverted_index.implementation | 38 |
| abstract_inverted_index.learning-based | 27 |
| abstract_inverted_index.well-recognized | 131 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.1498146 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |