Discrete Ripplet-II Transform Feature Extraction and Metaheuristic-Optimized Feature Selection for Enhanced Glaucoma Detection in Fundus Images Using LS-SVM Article Swipe
Santosh Kumar Sharma
,
Debendra Muduli
,
Adyasha Rath
,
S. Dash
,
Ganapati Panda
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202311.0773.v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202311.0773.v1
Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and their variations, along with classifier like support vector machines (SVM), are frequently employed in such systems. This paper introduces a practical and enhanced system for detecting glaucoma in fundus images. This system adresses the chanallages encountered by other existing models in recent litrature. Initially, we have employed contrast limited adaputive histogram equalization (CLAHE) to enhanced the visualization of input fundus inmages. Then, the discrete ripplet-II transform (DR2T) employing a degree of 2 for feature extraction. Subsequently, a golden jackal optimization algorithm (GJO) employed to select the optimal features to reduce the dimension of the extracted feature vector. During the classification stage the least square support vector machine (LS-SVM) with three kernels called as linear, polynomial and radial basis function(RBF), for classifying of fundus images as glaucoma or healthy. The proposed method is validated with the current state-of-the-art models on two standard datasets, namely, G1020 and ORIGA. The results obtained from our experimental result demonstrate that our best suggested approach DR2T+GJO+LS-SVM-RBF obtains better classification accuracy 93.38% and 97.31% for G1020 and ORIGA dataset with less number of features. It establishes a more concise network structure when contrasted with traditional classifiers.
Related Topics
Concepts
Artificial intelligence
Support vector machine
Pattern recognition (psychology)
Computer science
Adaptive histogram equalization
Feature extraction
Histogram
Radial basis function
Feature selection
Histogram equalization
Artificial neural network
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202311.0773.v1
- https://www.preprints.org/manuscript/202311.0773/v1/download
- OA Status
- green
- Cited By
- 3
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388646653
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388646653Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202311.0773.v1Digital Object Identifier
- Title
-
Discrete Ripplet-II Transform Feature Extraction and Metaheuristic-Optimized Feature Selection for Enhanced Glaucoma Detection in Fundus Images Using LS-SVMWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-13Full publication date if available
- Authors
-
Santosh Kumar Sharma, Debendra Muduli, Adyasha Rath, S. Dash, Ganapati PandaList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202311.0773.v1Publisher landing page
- PDF URL
-
https://www.preprints.org/manuscript/202311.0773/v1/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.preprints.org/manuscript/202311.0773/v1/downloadDirect OA link when available
- Concepts
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Artificial intelligence, Support vector machine, Pattern recognition (psychology), Computer science, Adaptive histogram equalization, Feature extraction, Histogram, Radial basis function, Feature selection, Histogram equalization, Artificial neural network, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2420085356, https://openalex.org/W2143189496, https://openalex.org/W2183159415, https://openalex.org/W4212813292, https://openalex.org/W4231555510, https://openalex.org/W4200014273, https://openalex.org/W2627028372, https://openalex.org/W3156834475, https://openalex.org/W4249343796, https://openalex.org/W2184652140, https://openalex.org/W3143791711, https://openalex.org/W3163925244, https://openalex.org/W2973289689, https://openalex.org/W4200169950, https://openalex.org/W2300684981, https://openalex.org/W2183593162, https://openalex.org/W4231282021, https://openalex.org/W4366386504, https://openalex.org/W2072451313, https://openalex.org/W3178773782, https://openalex.org/W3010419441, https://openalex.org/W3183844289, https://openalex.org/W2770475823, https://openalex.org/W2791059692, https://openalex.org/W4376612294, https://openalex.org/W3101386228, https://openalex.org/W2965408167, https://openalex.org/W4379931722, https://openalex.org/W2187731819, https://openalex.org/W4388318134, https://openalex.org/W1921244095, https://openalex.org/W2538579510, https://openalex.org/W4386889652, https://openalex.org/W2115528090, https://openalex.org/W3152435479, https://openalex.org/W4289936402, https://openalex.org/W1971808213, https://openalex.org/W4366375475, https://openalex.org/W4312399698, https://openalex.org/W4378227582, https://openalex.org/W2330025265, https://openalex.org/W2204969442, https://openalex.org/W3016810854, https://openalex.org/W4327808499, https://openalex.org/W4320919595, https://openalex.org/W4372049038, https://openalex.org/W2114910241, https://openalex.org/W2673612352, https://openalex.org/W4236907415, https://openalex.org/W2583672578, https://openalex.org/W2096741933, https://openalex.org/W3149581466, https://openalex.org/W3094504384, https://openalex.org/W2779238497, https://openalex.org/W1986086122, https://openalex.org/W4280572946, https://openalex.org/W4283706967, https://openalex.org/W4200175812, https://openalex.org/W2161811007, https://openalex.org/W1485280399 |
| referenced_works_count | 60 |
| abstract_inverted_index.2 | 101 |
| abstract_inverted_index.a | 49, 98, 106, 209 |
| abstract_inverted_index.It | 207 |
| abstract_inverted_index.as | 27, 142, 154 |
| abstract_inverted_index.by | 66 |
| abstract_inverted_index.in | 6, 43, 57, 70 |
| abstract_inverted_index.is | 161 |
| abstract_inverted_index.of | 87, 100, 122, 151, 205 |
| abstract_inverted_index.on | 168 |
| abstract_inverted_index.or | 156 |
| abstract_inverted_index.to | 83, 113, 118 |
| abstract_inverted_index.we | 74 |
| abstract_inverted_index.The | 158, 176 |
| abstract_inverted_index.and | 29, 51, 145, 174, 195, 199 |
| abstract_inverted_index.are | 40 |
| abstract_inverted_index.for | 12, 23, 54, 102, 149, 197 |
| abstract_inverted_index.has | 3 |
| abstract_inverted_index.our | 180, 185 |
| abstract_inverted_index.the | 63, 85, 92, 115, 120, 123, 128, 131, 164 |
| abstract_inverted_index.two | 169 |
| abstract_inverted_index.This | 46, 60 |
| abstract_inverted_index.been | 4 |
| abstract_inverted_index.best | 186 |
| abstract_inverted_index.from | 179 |
| abstract_inverted_index.have | 75 |
| abstract_inverted_index.less | 203 |
| abstract_inverted_index.like | 35 |
| abstract_inverted_index.made | 5 |
| abstract_inverted_index.more | 210 |
| abstract_inverted_index.such | 26, 44 |
| abstract_inverted_index.that | 184 |
| abstract_inverted_index.when | 214 |
| abstract_inverted_index.with | 33, 138, 163, 202, 216 |
| abstract_inverted_index.(CAD) | 10 |
| abstract_inverted_index.(GJO) | 111 |
| abstract_inverted_index.G1020 | 173, 198 |
| abstract_inverted_index.ORIGA | 200 |
| abstract_inverted_index.Then, | 91 |
| abstract_inverted_index.along | 32 |
| abstract_inverted_index.basis | 147 |
| abstract_inverted_index.input | 88 |
| abstract_inverted_index.least | 132 |
| abstract_inverted_index.other | 67 |
| abstract_inverted_index.paper | 47 |
| abstract_inverted_index.stage | 130 |
| abstract_inverted_index.their | 20, 30 |
| abstract_inverted_index.three | 139 |
| abstract_inverted_index.using | 16 |
| abstract_inverted_index.(DR2T) | 96 |
| abstract_inverted_index.(SVM), | 39 |
| abstract_inverted_index.93.38% | 194 |
| abstract_inverted_index.97.31% | 196 |
| abstract_inverted_index.During | 127 |
| abstract_inverted_index.ORIGA. | 175 |
| abstract_inverted_index.better | 191 |
| abstract_inverted_index.called | 141 |
| abstract_inverted_index.degree | 99 |
| abstract_inverted_index.fundus | 17, 58, 89, 152 |
| abstract_inverted_index.golden | 107 |
| abstract_inverted_index.images | 153 |
| abstract_inverted_index.jackal | 108 |
| abstract_inverted_index.method | 160 |
| abstract_inverted_index.models | 69, 167 |
| abstract_inverted_index.number | 204 |
| abstract_inverted_index.radial | 146 |
| abstract_inverted_index.recent | 71 |
| abstract_inverted_index.reduce | 119 |
| abstract_inverted_index.result | 182 |
| abstract_inverted_index.select | 114 |
| abstract_inverted_index.square | 133 |
| abstract_inverted_index.system | 53, 61 |
| abstract_inverted_index.vector | 37, 135 |
| abstract_inverted_index.(CLAHE) | 82 |
| abstract_inverted_index.Despite | 19 |
| abstract_inverted_index.concise | 211 |
| abstract_inverted_index.current | 165 |
| abstract_inverted_index.dataset | 201 |
| abstract_inverted_index.feature | 103, 125 |
| abstract_inverted_index.images. | 18, 59 |
| abstract_inverted_index.kernels | 140 |
| abstract_inverted_index.limited | 78 |
| abstract_inverted_index.linear, | 143 |
| abstract_inverted_index.machine | 136 |
| abstract_inverted_index.methods | 22 |
| abstract_inverted_index.namely, | 172 |
| abstract_inverted_index.network | 212 |
| abstract_inverted_index.obtains | 190 |
| abstract_inverted_index.optimal | 116 |
| abstract_inverted_index.results | 177 |
| abstract_inverted_index.support | 36, 134 |
| abstract_inverted_index.systems | 11 |
| abstract_inverted_index.vector. | 126 |
| abstract_inverted_index.(LS-SVM) | 137 |
| abstract_inverted_index.accuracy | 193 |
| abstract_inverted_index.adresses | 62 |
| abstract_inverted_index.approach | 188 |
| abstract_inverted_index.contrast | 77 |
| abstract_inverted_index.discrete | 93 |
| abstract_inverted_index.employed | 42, 76, 112 |
| abstract_inverted_index.enhanced | 52, 84 |
| abstract_inverted_index.existing | 68 |
| abstract_inverted_index.features | 25, 117 |
| abstract_inverted_index.glaucoma | 14, 56, 155 |
| abstract_inverted_index.healthy. | 157 |
| abstract_inverted_index.inmages. | 90 |
| abstract_inverted_index.machines | 38 |
| abstract_inverted_index.obtained | 178 |
| abstract_inverted_index.progress | 2 |
| abstract_inverted_index.proposed | 159 |
| abstract_inverted_index.standard | 170 |
| abstract_inverted_index.systems. | 45 |
| abstract_inverted_index.wavelets | 28 |
| abstract_inverted_index.Recently, | 0 |
| abstract_inverted_index.adaputive | 79 |
| abstract_inverted_index.algorithm | 110 |
| abstract_inverted_index.datasets, | 171 |
| abstract_inverted_index.detecting | 55 |
| abstract_inverted_index.diagnosis | 9 |
| abstract_inverted_index.dimension | 121 |
| abstract_inverted_index.employing | 97 |
| abstract_inverted_index.extracted | 124 |
| abstract_inverted_index.features. | 206 |
| abstract_inverted_index.histogram | 80 |
| abstract_inverted_index.practical | 50 |
| abstract_inverted_index.structure | 213 |
| abstract_inverted_index.suggested | 187 |
| abstract_inverted_index.transform | 95 |
| abstract_inverted_index.validated | 162 |
| abstract_inverted_index.Initially, | 73 |
| abstract_inverted_index.classifier | 34 |
| abstract_inverted_index.contrasted | 215 |
| abstract_inverted_index.developing | 7 |
| abstract_inverted_index.drawbacks, | 21 |
| abstract_inverted_index.extracting | 24 |
| abstract_inverted_index.frequently | 41 |
| abstract_inverted_index.introduces | 48 |
| abstract_inverted_index.litrature. | 72 |
| abstract_inverted_index.polynomial | 144 |
| abstract_inverted_index.ripplet-II | 94 |
| abstract_inverted_index.chanallages | 64 |
| abstract_inverted_index.classifying | 150 |
| abstract_inverted_index.demonstrate | 183 |
| abstract_inverted_index.encountered | 65 |
| abstract_inverted_index.establishes | 208 |
| abstract_inverted_index.extraction. | 104 |
| abstract_inverted_index.identifying | 13 |
| abstract_inverted_index.significant | 1 |
| abstract_inverted_index.traditional | 217 |
| abstract_inverted_index.variations, | 31 |
| abstract_inverted_index.classifiers. | 218 |
| abstract_inverted_index.equalization | 81 |
| abstract_inverted_index.experimental | 181 |
| abstract_inverted_index.optimization | 109 |
| abstract_inverted_index.Subsequently, | 105 |
| abstract_inverted_index.abnormalities | 15 |
| abstract_inverted_index.visualization | 86 |
| abstract_inverted_index.classification | 129, 192 |
| abstract_inverted_index.computer-aided | 8 |
| abstract_inverted_index.function(RBF), | 148 |
| abstract_inverted_index.state-of-the-art | 166 |
| abstract_inverted_index.DR2T+GJO+LS-SVM-RBF | 189 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.75074871 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |