Prediction of Lung Cancer using Ensemble Classifiers Article Swipe
Gautam Shanbhag
,
K. Prabhu
,
N. V. Subba Reddy
,
B. Ashwath Rao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012007
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012007
Carcinoma detection from CT scan images is extremely necessary for numerous diagnostic and healing applications. Because of the excessive amount of information in CT scan images and blurred boundaries, tumor segmentation and class are extremely laborious. The intention is to categorize carcinoma into benign and malignant categories. In MR pictures, the number of facts is a lot for interpreting and evaluating manually. Over the previous few years, carcinoma detection in CT has grown to be a rising evaluation space in the area of the scientific imaging system. Correct detection of length and site of lung cancer performs a vital position in the designation of carcinoma. In this paper, we introduce a novel carcinoma detection methodology that helps in predicting the carcinoma from the CT scanned images. The methodology has 4 different stages, pre-processing the image data, segmentation, extracting features, and classification stage to categorize the benign and malignant. This work makes use of extraordinary models for detecting carcinoma in a CT test via way of means of constructing an ensemble classifier. Techniques proposed in the paper helped us achieve an accuracy of 85% using Ensemble-Classifier which showcases that model has the capability of predicting the malignant cases correctly. The ensemble classifier consists of 5 machine learning models like SVM, LR, MLP, decision tree, and KNN. The inevitable parameters like accuracy, recall, and precision is calculated to determine the accurate results of the classifier.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2161/1/012007
- https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012007/pdf
- OA Status
- diamond
- Cited By
- 23
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4206678852
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4206678852Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2161/1/012007Digital Object Identifier
- Title
-
Prediction of Lung Cancer using Ensemble ClassifiersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Gautam Shanbhag, K. Prabhu, N. V. Subba Reddy, B. Ashwath RaoList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2161/1/012007Publisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012007/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012007/pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer science, Classifier (UML), Support vector machine, Pattern recognition (psychology), Decision tree, Categorization, Segmentation, Ensemble learning, Random forest, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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23Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 11, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 18, 51, 64, 81, 84, 102, 120, 123, 134, 145, 175, 191, 195, 228, 232 |
| abstract_inverted_index.use | 152 |
| abstract_inverted_index.via | 163 |
| abstract_inverted_index.way | 164 |
| abstract_inverted_index.KNN. | 215 |
| abstract_inverted_index.MLP, | 211 |
| abstract_inverted_index.Over | 63 |
| abstract_inverted_index.SVM, | 209 |
| abstract_inverted_index.This | 149 |
| abstract_inverted_index.area | 82 |
| abstract_inverted_index.from | 3, 122 |
| abstract_inverted_index.into | 43 |
| abstract_inverted_index.like | 208, 219 |
| abstract_inverted_index.lung | 95 |
| abstract_inverted_index.scan | 5, 25 |
| abstract_inverted_index.site | 93 |
| abstract_inverted_index.test | 162 |
| abstract_inverted_index.that | 116, 188 |
| abstract_inverted_index.this | 107 |
| abstract_inverted_index.work | 150 |
| abstract_inverted_index.cases | 197 |
| abstract_inverted_index.class | 33 |
| abstract_inverted_index.data, | 136 |
| abstract_inverted_index.facts | 54 |
| abstract_inverted_index.grown | 73 |
| abstract_inverted_index.helps | 117 |
| abstract_inverted_index.image | 135 |
| abstract_inverted_index.makes | 151 |
| abstract_inverted_index.means | 166 |
| abstract_inverted_index.model | 189 |
| abstract_inverted_index.novel | 112 |
| abstract_inverted_index.paper | 176 |
| abstract_inverted_index.space | 79 |
| abstract_inverted_index.stage | 142 |
| abstract_inverted_index.tree, | 213 |
| abstract_inverted_index.tumor | 30 |
| abstract_inverted_index.using | 184 |
| abstract_inverted_index.vital | 99 |
| abstract_inverted_index.which | 186 |
| abstract_inverted_index.amount | 20 |
| abstract_inverted_index.benign | 44, 146 |
| abstract_inverted_index.cancer | 96 |
| abstract_inverted_index.helped | 177 |
| abstract_inverted_index.images | 6, 26 |
| abstract_inverted_index.length | 91 |
| abstract_inverted_index.models | 155, 207 |
| abstract_inverted_index.number | 52 |
| abstract_inverted_index.paper, | 108 |
| abstract_inverted_index.rising | 77 |
| abstract_inverted_index.years, | 67 |
| abstract_inverted_index.Because | 16 |
| abstract_inverted_index.Correct | 88 |
| abstract_inverted_index.achieve | 179 |
| abstract_inverted_index.blurred | 28 |
| abstract_inverted_index.healing | 14 |
| abstract_inverted_index.images. | 126 |
| abstract_inverted_index.imaging | 86 |
| abstract_inverted_index.machine | 205 |
| abstract_inverted_index.recall, | 221 |
| abstract_inverted_index.results | 230 |
| abstract_inverted_index.scanned | 125 |
| abstract_inverted_index.stages, | 132 |
| abstract_inverted_index.system. | 87 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accuracy | 181 |
| abstract_inverted_index.accurate | 229 |
| abstract_inverted_index.consists | 202 |
| abstract_inverted_index.decision | 212 |
| abstract_inverted_index.ensemble | 170, 200 |
| abstract_inverted_index.learning | 206 |
| abstract_inverted_index.numerous | 11 |
| abstract_inverted_index.performs | 97 |
| abstract_inverted_index.position | 100 |
| abstract_inverted_index.previous | 65 |
| abstract_inverted_index.proposed | 173 |
| abstract_inverted_index.Carcinoma | 1 |
| abstract_inverted_index.accuracy, | 220 |
| abstract_inverted_index.carcinoma | 42, 68, 113, 121, 158 |
| abstract_inverted_index.detecting | 157 |
| abstract_inverted_index.detection | 2, 69, 89, 114 |
| abstract_inverted_index.determine | 227 |
| abstract_inverted_index.different | 131 |
| abstract_inverted_index.excessive | 19 |
| abstract_inverted_index.extremely | 8, 35 |
| abstract_inverted_index.features, | 139 |
| abstract_inverted_index.intention | 38 |
| abstract_inverted_index.introduce | 110 |
| abstract_inverted_index.malignant | 46, 196 |
| abstract_inverted_index.manually. | 62 |
| abstract_inverted_index.necessary | 9 |
| abstract_inverted_index.pictures, | 50 |
| abstract_inverted_index.precision | 223 |
| abstract_inverted_index.showcases | 187 |
| abstract_inverted_index.Techniques | 172 |
| abstract_inverted_index.calculated | 225 |
| abstract_inverted_index.capability | 192 |
| abstract_inverted_index.carcinoma. | 105 |
| abstract_inverted_index.categorize | 41, 144 |
| abstract_inverted_index.classifier | 201 |
| abstract_inverted_index.correctly. | 198 |
| abstract_inverted_index.diagnostic | 12 |
| abstract_inverted_index.evaluating | 61 |
| abstract_inverted_index.evaluation | 78 |
| abstract_inverted_index.extracting | 138 |
| abstract_inverted_index.inevitable | 217 |
| abstract_inverted_index.laborious. | 36 |
| abstract_inverted_index.malignant. | 148 |
| abstract_inverted_index.parameters | 218 |
| abstract_inverted_index.predicting | 119, 194 |
| abstract_inverted_index.scientific | 85 |
| abstract_inverted_index.boundaries, | 29 |
| abstract_inverted_index.categories. | 47 |
| abstract_inverted_index.classifier. | 171, 233 |
| abstract_inverted_index.designation | 103 |
| abstract_inverted_index.information | 22 |
| abstract_inverted_index.methodology | 115, 128 |
| abstract_inverted_index.constructing | 168 |
| abstract_inverted_index.interpreting | 59 |
| abstract_inverted_index.segmentation | 31 |
| abstract_inverted_index.applications. | 15 |
| abstract_inverted_index.extraordinary | 154 |
| abstract_inverted_index.segmentation, | 137 |
| abstract_inverted_index.classification | 141 |
| abstract_inverted_index.pre-processing | 133 |
| abstract_inverted_index.Ensemble-Classifier | 185 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5003674124, https://openalex.org/A5027220210 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I164861460 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.92860706 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |