Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources Article Swipe
Tina Babu
,
Deepa Gupta
,
Tripty Singh
,
Shahin Hameed
,
Mohammed Zakariah
,
Yousef Ajami Alotaibi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2021.016341
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2021.016341
Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest (BO-RF) classifiers. This study uses four datasets (two collected from Indian hospitals—Ishita Pathology Center (IPC) of 4X, 10X, and 40X and Aster Medcity (AMC) of 10X, 20X, and 40X—two benchmark datasets—gland segmentation (GlaS) of 20X and IMEDIATREAT of 10X) comprising multiple microscope magnifications. Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy (97.25%-IPC, 94.40%-AMC, 97.58%-GlaS, 99.16%-Imediatreat), sensitivity (0.9725-IPC, 0.9440-AMC, 0.9807-GlaS, 0.9923-Imediatreat), specificity (0.9908-IPC, 0.9813-AMC, 0.9907-GlaS, 0.9971-Imediatreat) and F-score (0.9725-IPC, 0.9441-AMC, 0.9780-GlaS, 0.9923-Imediatreat). The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset, highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2021.016341
- https://www.techscience.com/ueditor/files/cmc/TSP_CMC_69-1/TSP_CMC_16341/TSP_CMC_16341.pdf
- OA Status
- diamond
- Cited By
- 59
- References
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3168379340Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2021.016341Digital Object Identifier
- Title
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Robust Magnification Independent Colon Biopsy Grading System over Multiple Data SourcesWork 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
- Publication date
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2021-01-01Full publication date if available
- Authors
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Tina Babu, Deepa Gupta, Tripty Singh, Shahin Hameed, Mohammed Zakariah, Yousef Ajami AlotaibiList of authors in order
- Landing page
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https://doi.org/10.32604/cmc.2021.016341Publisher landing page
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https://www.techscience.com/ueditor/files/cmc/TSP_CMC_69-1/TSP_CMC_16341/TSP_CMC_16341.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.techscience.com/ueditor/files/cmc/TSP_CMC_69-1/TSP_CMC_16341/TSP_CMC_16341.pdfDirect OA link when available
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Magnification, Artificial intelligence, Pattern recognition (psychology), Grading (engineering), Segmentation, Histogram, Computer science, Wavelet, Mathematics, Image (mathematics), Civil engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
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59Total citation count in OpenAlex
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2025: 19, 2024: 23, 2023: 10, 2022: 7Per-year citation counts (last 5 years)
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58Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6754669450, https://openalex.org/W6674970907, https://openalex.org/W2131594686, https://openalex.org/W2057114171, https://openalex.org/W2612373502, https://openalex.org/W2103243046, https://openalex.org/W3094425138, https://openalex.org/W2167533099, https://openalex.org/W755307704, https://openalex.org/W2397772398, https://openalex.org/W6772295539, https://openalex.org/W6750783924, https://openalex.org/W6749088015, https://openalex.org/W4241382889, https://openalex.org/W2788712375, https://openalex.org/W2985641961, https://openalex.org/W1950315773, https://openalex.org/W6787159789, https://openalex.org/W3119071616, https://openalex.org/W2504150216, https://openalex.org/W2762006829, https://openalex.org/W2618999197, https://openalex.org/W2769999077, https://openalex.org/W2910543518, https://openalex.org/W2344480160, https://openalex.org/W3004016611, https://openalex.org/W2435090885, https://openalex.org/W2761668009, https://openalex.org/W6763426159, https://openalex.org/W6726459654, https://openalex.org/W2976786915, https://openalex.org/W3009334705, https://openalex.org/W2916471783, https://openalex.org/W1977885560, https://openalex.org/W2091151456, https://openalex.org/W2164500538, https://openalex.org/W2896683552, https://openalex.org/W2282971418, https://openalex.org/W2091628730, https://openalex.org/W6640142374, https://openalex.org/W2921338250, https://openalex.org/W2938285755, https://openalex.org/W1809990924, https://openalex.org/W6766576792, https://openalex.org/W6634147026, https://openalex.org/W2126727011, https://openalex.org/W2783710041, https://openalex.org/W6760024990, https://openalex.org/W2274287116, https://openalex.org/W2001412060, https://openalex.org/W3113026834, https://openalex.org/W2964350391, https://openalex.org/W2100125385, https://openalex.org/W2889646458, https://openalex.org/W3103568526, https://openalex.org/W2967663220, https://openalex.org/W2947660042, https://openalex.org/W1573647811 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 24, 55, 91 |
| abstract_inverted_index.as | 90 |
| abstract_inverted_index.be | 79 |
| abstract_inverted_index.by | 197 |
| abstract_inverted_index.in | 161, 199, 204, 210, 218 |
| abstract_inverted_index.is | 9, 52, 88, 195 |
| abstract_inverted_index.of | 2, 12, 27, 49, 117, 126, 135, 139, 163, 187, 222 |
| abstract_inverted_index.on | 18 |
| abstract_inverted_index.to | 34, 53, 81, 190 |
| abstract_inverted_index.20X | 136 |
| abstract_inverted_index.40X | 121 |
| abstract_inverted_index.4X, | 118 |
| abstract_inverted_index.HSV | 68 |
| abstract_inverted_index.The | 47, 185 |
| abstract_inverted_index.all | 7 |
| abstract_inverted_index.and | 15, 45, 74, 120, 122, 129, 137, 179, 202, 213, 226 |
| abstract_inverted_index.any | 191 |
| abstract_inverted_index.can | 78 |
| abstract_inverted_index.for | 157 |
| abstract_inverted_index.its | 215 |
| abstract_inverted_index.one | 200 |
| abstract_inverted_index.set | 60 |
| abstract_inverted_index.six | 95 |
| abstract_inverted_index.the | 86, 149, 153, 188, 219 |
| abstract_inverted_index.use | 217 |
| abstract_inverted_index.(two | 109 |
| abstract_inverted_index.10X) | 140 |
| abstract_inverted_index.10X, | 119, 127 |
| abstract_inverted_index.20X, | 128 |
| abstract_inverted_index.This | 21, 104 |
| abstract_inverted_index.each | 19 |
| abstract_inverted_index.four | 40, 107 |
| abstract_inverted_index.from | 111 |
| abstract_inverted_index.into | 39 |
| abstract_inverted_index.that | 77, 148 |
| abstract_inverted_index.this | 50 |
| abstract_inverted_index.used | 80, 156 |
| abstract_inverted_index.uses | 106 |
| abstract_inverted_index.with | 94 |
| abstract_inverted_index.work | 22 |
| abstract_inverted_index.(AMC) | 125 |
| abstract_inverted_index.(IPC) | 116 |
| abstract_inverted_index.Aster | 123 |
| abstract_inverted_index.Gabor | 64 |
| abstract_inverted_index.class | 97 |
| abstract_inverted_index.colon | 3, 30, 36, 158 |
| abstract_inverted_index.color | 70, 72 |
| abstract_inverted_index.first | 220 |
| abstract_inverted_index.image | 194 |
| abstract_inverted_index.input | 193 |
| abstract_inverted_index.level | 221 |
| abstract_inverted_index.model | 189 |
| abstract_inverted_index.novel | 25 |
| abstract_inverted_index.other | 154 |
| abstract_inverted_index.poor. | 46 |
| abstract_inverted_index.study | 105 |
| abstract_inverted_index.terms | 162 |
| abstract_inverted_index.well, | 43 |
| abstract_inverted_index.(GlaS) | 134 |
| abstract_inverted_index.Center | 115 |
| abstract_inverted_index.Indian | 112 |
| abstract_inverted_index.across | 6 |
| abstract_inverted_index.binary | 96 |
| abstract_inverted_index.biopsy | 4, 37, 224 |
| abstract_inverted_index.cancer | 31, 159 |
| abstract_inverted_index.forest | 101 |
| abstract_inverted_index.hybrid | 58 |
| abstract_inverted_index.images | 5, 38 |
| abstract_inverted_index.method | 151 |
| abstract_inverted_index.random | 100 |
| abstract_inverted_index.robust | 28 |
| abstract_inverted_index.second | 227 |
| abstract_inverted_index.strong | 208 |
| abstract_inverted_index.(BO-RF) | 102 |
| abstract_inverted_index.F-score | 180 |
| abstract_inverted_index.Medcity | 124 |
| abstract_inverted_index.another | 205 |
| abstract_inverted_index.because | 11 |
| abstract_inverted_index.cartoon | 62 |
| abstract_inverted_index.dataset | 201 |
| abstract_inverted_index.develop | 54 |
| abstract_inverted_index.feature | 59 |
| abstract_inverted_index.grades. | 84 |
| abstract_inverted_index.grading | 1, 32, 160, 225 |
| abstract_inverted_index.methods | 155 |
| abstract_inverted_index.modeled | 89 |
| abstract_inverted_index.normal, | 42 |
| abstract_inverted_index.results | 146 |
| abstract_inverted_index.testing | 203 |
| abstract_inverted_index.wavelet | 66 |
| abstract_inverted_index.Bayesian | 98 |
| abstract_inverted_index.Besides, | 85 |
| abstract_inverted_index.accuracy | 164 |
| abstract_inverted_index.approach | 26 |
| abstract_inverted_index.classes: | 41 |
| abstract_inverted_index.dataset, | 206 |
| abstract_inverted_index.datasets | 108 |
| abstract_inverted_index.feature, | 63 |
| abstract_inverted_index.features | 17, 76 |
| abstract_inverted_index.moments, | 67, 73 |
| abstract_inverted_index.multiple | 142 |
| abstract_inverted_index.opinion. | 228 |
| abstract_inverted_index.presents | 23 |
| abstract_inverted_index.proposed | 150 |
| abstract_inverted_index.research | 51 |
| abstract_inverted_index.tailored | 13 |
| abstract_inverted_index.training | 198 |
| abstract_inverted_index.wavelet, | 65 |
| abstract_inverted_index.40X—two | 130 |
| abstract_inverted_index.Automated | 0 |
| abstract_inverted_index.Pathology | 114 |
| abstract_inverted_index.automatic | 223 |
| abstract_inverted_index.benchmark | 131 |
| abstract_inverted_index.collected | 110 |
| abstract_inverted_index.dependent | 16 |
| abstract_inverted_index.different | 83 |
| abstract_inverted_index.effective | 216 |
| abstract_inverted_index.framework | 33 |
| abstract_inverted_index.invariant | 57 |
| abstract_inverted_index.magnified | 192 |
| abstract_inverted_index.moderate, | 44 |
| abstract_inverted_index.optimized | 99 |
| abstract_inverted_index.structure | 93 |
| abstract_inverted_index.validated | 196 |
| abstract_inverted_index.classifier | 87 |
| abstract_inverted_index.comprising | 61, 141 |
| abstract_inverted_index.evidencing | 214 |
| abstract_inverted_index.histogram, | 69 |
| abstract_inverted_index.microscope | 143 |
| abstract_inverted_index.multiclass | 92, 211 |
| abstract_inverted_index.0.9440-AMC, | 171 |
| abstract_inverted_index.0.9441-AMC, | 182 |
| abstract_inverted_index.0.9813-AMC, | 176 |
| abstract_inverted_index.94.40%-AMC, | 166 |
| abstract_inverted_index.IMEDIATREAT | 138 |
| abstract_inverted_index.challenging | 10 |
| abstract_inverted_index.concordance | 209 |
| abstract_inverted_index.demonstrate | 147 |
| abstract_inverted_index.distinguish | 35 |
| abstract_inverted_index.outperforms | 152 |
| abstract_inverted_index.sensitivity | 169 |
| abstract_inverted_index.specificity | 174 |
| abstract_inverted_index.(0.9725-IPC, | 170, 181 |
| abstract_inverted_index.(0.9908-IPC, | 175 |
| abstract_inverted_index.(97.25%-IPC, | 165 |
| abstract_inverted_index.0.9780-GlaS, | 183 |
| abstract_inverted_index.0.9807-GlaS, | 172 |
| abstract_inverted_index.0.9907-GlaS, | 177 |
| abstract_inverted_index.97.58%-GlaS, | 167 |
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| abstract_inverted_index.characterize | 82 |
| abstract_inverted_index.classifiers. | 103 |
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| abstract_inverted_index.highlighting | 207 |
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| abstract_inverted_index.morphological | 75 |
| abstract_inverted_index.classification | 212 |
| abstract_inverted_index.magnification. | 20 |
| abstract_inverted_index.magnifications | 8 |
| abstract_inverted_index.magnifications. | 144 |
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| abstract_inverted_index.generalizability | 186 |
| abstract_inverted_index.auto-correlogram, | 71 |
| abstract_inverted_index.hospitals—Ishita | 113 |
| abstract_inverted_index.0.9971-Imediatreat) | 178 |
| abstract_inverted_index.0.9923-Imediatreat), | 173 |
| abstract_inverted_index.0.9923-Imediatreat). | 184 |
| abstract_inverted_index.99.16%-Imediatreat), | 168 |
| abstract_inverted_index.magnification-independent | 29 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5022739741 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I81556334 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.96611044 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |