Bronchop Neumonia Detection Using Novel Multilevel Deep Neural Network Schema Article Swipe
K. R. Prasanna Kumar
,
R. Vijayakumar
,
Joseph Durai Sevam
,
P. Parthasarathy
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/e3sconf/202339909001
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/e3sconf/202339909001
Pneumonia is a dangerous disease that can occur in one or both lungs and is usually caused by a virus, fungus or bacteria. Respiratory syncytial virus (RSV) is the most common cause of pneumonia in children. With the development of pneumonia, it can be divided into four stages: congestion, red liver, gray liver and regression. In our work, we employ the most powerful tools and techniques such as VGG16, an object recognition and classification algorithm that can classify 1000 images in 1000 different groups with 92.7% accuracy. It is one of the popular algorithms designed for image classification and simple to use by means of transfer learning. Transfer learning (TL) is a technique in deep learning that spotlight on pre-learning the neural network and storing the knowledge gained while solving a problem and applying it to new and different information. In our work, the information gained by learning about 1000 different groups on Image Net can be used and strive to identify diseases.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/e3sconf/202339909001
- https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_09001.pdf
- OA Status
- diamond
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384572180
All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.1051/e3sconf/202339909001Digital Object Identifier
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Bronchop Neumonia Detection Using Novel Multilevel Deep Neural Network SchemaWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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K. R. Prasanna Kumar, R. Vijayakumar, Joseph Durai Sevam, P. ParthasarathyList of authors in order
- Landing page
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https://doi.org/10.1051/e3sconf/202339909001Publisher landing page
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https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_09001.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_09001.pdfDirect OA link when available
- Concepts
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Deep learning, Computer science, Transfer of learning, Artificial intelligence, Schema (genetic algorithms), Machine learning, Artificial neural network, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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16Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.most | 29, 61 |
| abstract_inverted_index.such | 66 |
| abstract_inverted_index.that | 5, 75, 116 |
| abstract_inverted_index.used | 157 |
| abstract_inverted_index.with | 84 |
| abstract_inverted_index.(RSV) | 26 |
| abstract_inverted_index.92.7% | 85 |
| abstract_inverted_index.Image | 153 |
| abstract_inverted_index.about | 148 |
| abstract_inverted_index.cause | 31 |
| abstract_inverted_index.image | 96 |
| abstract_inverted_index.liver | 52 |
| abstract_inverted_index.lungs | 12 |
| abstract_inverted_index.means | 103 |
| abstract_inverted_index.occur | 7 |
| abstract_inverted_index.tools | 63 |
| abstract_inverted_index.virus | 25 |
| abstract_inverted_index.while | 128 |
| abstract_inverted_index.work, | 57, 142 |
| abstract_inverted_index.VGG16, | 68 |
| abstract_inverted_index.caused | 16 |
| abstract_inverted_index.common | 30 |
| abstract_inverted_index.employ | 59 |
| abstract_inverted_index.fungus | 20 |
| abstract_inverted_index.gained | 127, 145 |
| abstract_inverted_index.groups | 83, 151 |
| abstract_inverted_index.images | 79 |
| abstract_inverted_index.liver, | 50 |
| abstract_inverted_index.neural | 121 |
| abstract_inverted_index.object | 70 |
| abstract_inverted_index.simple | 99 |
| abstract_inverted_index.strive | 159 |
| abstract_inverted_index.virus, | 19 |
| abstract_inverted_index.disease | 4 |
| abstract_inverted_index.divided | 44 |
| abstract_inverted_index.network | 122 |
| abstract_inverted_index.popular | 92 |
| abstract_inverted_index.problem | 131 |
| abstract_inverted_index.solving | 129 |
| abstract_inverted_index.stages: | 47 |
| abstract_inverted_index.storing | 124 |
| abstract_inverted_index.usually | 15 |
| abstract_inverted_index.Transfer | 107 |
| abstract_inverted_index.applying | 133 |
| abstract_inverted_index.classify | 77 |
| abstract_inverted_index.designed | 94 |
| abstract_inverted_index.identify | 161 |
| abstract_inverted_index.learning | 108, 115, 147 |
| abstract_inverted_index.powerful | 62 |
| abstract_inverted_index.transfer | 105 |
| abstract_inverted_index.Pneumonia | 0 |
| abstract_inverted_index.accuracy. | 86 |
| abstract_inverted_index.algorithm | 74 |
| abstract_inverted_index.bacteria. | 22 |
| abstract_inverted_index.children. | 35 |
| abstract_inverted_index.dangerous | 3 |
| abstract_inverted_index.different | 82, 138, 150 |
| abstract_inverted_index.diseases. | 162 |
| abstract_inverted_index.knowledge | 126 |
| abstract_inverted_index.learning. | 106 |
| abstract_inverted_index.pneumonia | 33 |
| abstract_inverted_index.spotlight | 117 |
| abstract_inverted_index.syncytial | 24 |
| abstract_inverted_index.technique | 112 |
| abstract_inverted_index.algorithms | 93 |
| abstract_inverted_index.pneumonia, | 40 |
| abstract_inverted_index.techniques | 65 |
| abstract_inverted_index.Respiratory | 23 |
| abstract_inverted_index.congestion, | 48 |
| abstract_inverted_index.development | 38 |
| abstract_inverted_index.information | 144 |
| abstract_inverted_index.recognition | 71 |
| abstract_inverted_index.regression. | 54 |
| abstract_inverted_index.information. | 139 |
| abstract_inverted_index.pre-learning | 119 |
| abstract_inverted_index.classification | 73, 97 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.22188176 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |