Blood Vessel Segmentation and Classification for Diabetic Retinopathy Grading Using Dandelion Optimization Algorithm with Deep Learning Model Article Swipe
R. Ramesh
,
S. Sathiamoorthy
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22266/ijies2023.1031.02
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22266/ijies2023.1031.02
Diabetic retinopathy (DR) is a diabetic complexity that mainly affects the eye.Generally, an ophthalmologist defines the severity of the retinopathy by directly inspecting colour images and estimating them by visually examining the fundus.Due to the enormous amount of diabetic patients all over the world, it becomes an expensive process.The automated system was designed for accurate recognition of the disease using segmentation and fundus image.The blood vessel segmentation process is used to identify and separate blood vessels from the surrounding tissues in an image.This is a crucial stage in the detection of DR, as it enables physicians to measure and identify changes in blood vessels that are indicative of the disease.Numerous approaches are used for blood vessel segmentation and DR classification, along with deep learning (DL) and machine learning (ML) techniques.In this article, we introduce a novel dandelion optimization algorithm with deep learning based blood vessel segmentation and classification (DOADL-BVSC) model for DR grading.The presented DOADL-BVSC technique involves the concepts of blood vessel segmentation and DL-based classification for DR diagnosis on retinal fundus photographs.To accomplish this, the presented DOADL-BVSC technique uses the fuzzy set type-II approach for the image enhancement process.Next, U-Net with Bi-directional feature pyramid network (U-BFPN) model is exploited to segment the blood vessels in the retinal imaging effectively.Moreover, a squeeze and excitation (SE) network is applied for feature vector generation with DOA based hyperparameter optimizer.Finally, quantum autoencoder (QAE) approach is utilized for identifying and classifying DR into distinct stages.The experimental analysis of the DOADL-BVSC approach is investigated on benchmark DR datasets.The simulation results depicted the superior results of the DOADL-BVSC technique with increased accuracy of 99.93%.
Related Topics
Concepts
Computer science
Diabetic retinopathy
Grading (engineering)
Artificial intelligence
Dandelion
Segmentation
Algorithm
Optimization algorithm
Machine learning
Pattern recognition (psychology)
Mathematical optimization
Medicine
Diabetes mellitus
Mathematics
Pathology
Engineering
Alternative medicine
Civil engineering
Traditional Chinese medicine
Endocrinology
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.22266/ijies2023.1031.02
- https://doi.org/10.22266/ijies2023.1031.02
- OA Status
- bronze
- Cited By
- 3
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386132007
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386132007Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.22266/ijies2023.1031.02Digital Object Identifier
- Title
-
Blood Vessel Segmentation and Classification for Diabetic Retinopathy Grading Using Dandelion Optimization Algorithm with Deep Learning ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-24Full publication date if available
- Authors
-
R. Ramesh, S. SathiamoorthyList of authors in order
- Landing page
-
https://doi.org/10.22266/ijies2023.1031.02Publisher landing page
- PDF URL
-
https://doi.org/10.22266/ijies2023.1031.02Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://doi.org/10.22266/ijies2023.1031.02Direct OA link when available
- Concepts
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Computer science, Diabetic retinopathy, Grading (engineering), Artificial intelligence, Dandelion, Segmentation, Algorithm, Optimization algorithm, Machine learning, Pattern recognition (psychology), Mathematical optimization, Medicine, Diabetes mellitus, Mathematics, Pathology, Engineering, Alternative medicine, Civil engineering, Traditional Chinese medicine, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 10, 15, 18, 31, 34, 42, 57, 77, 88, 108, 157, 175, 180, 186, 202, 206, 244, 256, 260 |
| abstract_inverted_index.was | 51 |
| abstract_inverted_index.(DL) | 124 |
| abstract_inverted_index.(DR) | 2 |
| abstract_inverted_index.(ML) | 128 |
| abstract_inverted_index.(SE) | 214 |
| abstract_inverted_index.deep | 122, 140 |
| abstract_inverted_index.from | 76 |
| abstract_inverted_index.into | 238 |
| abstract_inverted_index.over | 41 |
| abstract_inverted_index.that | 7, 104 |
| abstract_inverted_index.them | 27 |
| abstract_inverted_index.this | 130 |
| abstract_inverted_index.used | 69, 112 |
| abstract_inverted_index.uses | 179 |
| abstract_inverted_index.with | 121, 139, 191, 222, 263 |
| abstract_inverted_index.(QAE) | 229 |
| abstract_inverted_index.U-Net | 190 |
| abstract_inverted_index.along | 120 |
| abstract_inverted_index.based | 142, 224 |
| abstract_inverted_index.blood | 64, 74, 102, 114, 143, 160, 203 |
| abstract_inverted_index.fuzzy | 181 |
| abstract_inverted_index.image | 187 |
| abstract_inverted_index.model | 149, 197 |
| abstract_inverted_index.novel | 135 |
| abstract_inverted_index.stage | 86 |
| abstract_inverted_index.this, | 174 |
| abstract_inverted_index.using | 59 |
| abstract_inverted_index.amount | 36 |
| abstract_inverted_index.colour | 23 |
| abstract_inverted_index.fundus | 62, 171 |
| abstract_inverted_index.images | 24 |
| abstract_inverted_index.mainly | 8 |
| abstract_inverted_index.system | 50 |
| abstract_inverted_index.vector | 220 |
| abstract_inverted_index.vessel | 65, 115, 144, 161 |
| abstract_inverted_index.world, | 43 |
| abstract_inverted_index.99.93%. | 267 |
| abstract_inverted_index.affects | 9 |
| abstract_inverted_index.applied | 217 |
| abstract_inverted_index.becomes | 45 |
| abstract_inverted_index.changes | 100 |
| abstract_inverted_index.crucial | 85 |
| abstract_inverted_index.defines | 14 |
| abstract_inverted_index.disease | 58 |
| abstract_inverted_index.enables | 94 |
| abstract_inverted_index.feature | 193, 219 |
| abstract_inverted_index.imaging | 208 |
| abstract_inverted_index.machine | 126 |
| abstract_inverted_index.measure | 97 |
| abstract_inverted_index.network | 195, 215 |
| abstract_inverted_index.process | 67 |
| abstract_inverted_index.pyramid | 194 |
| abstract_inverted_index.quantum | 227 |
| abstract_inverted_index.results | 254, 258 |
| abstract_inverted_index.retinal | 170, 207 |
| abstract_inverted_index.segment | 201 |
| abstract_inverted_index.squeeze | 211 |
| abstract_inverted_index.tissues | 79 |
| abstract_inverted_index.type-II | 183 |
| abstract_inverted_index.vessels | 75, 103, 204 |
| abstract_inverted_index.(U-BFPN) | 196 |
| abstract_inverted_index.DL-based | 164 |
| abstract_inverted_index.Diabetic | 0 |
| abstract_inverted_index.accuracy | 265 |
| abstract_inverted_index.accurate | 54 |
| abstract_inverted_index.analysis | 242 |
| abstract_inverted_index.approach | 184, 230, 246 |
| abstract_inverted_index.article, | 131 |
| abstract_inverted_index.concepts | 158 |
| abstract_inverted_index.depicted | 255 |
| abstract_inverted_index.designed | 52 |
| abstract_inverted_index.diabetic | 5, 38 |
| abstract_inverted_index.directly | 21 |
| abstract_inverted_index.distinct | 239 |
| abstract_inverted_index.enormous | 35 |
| abstract_inverted_index.identify | 71, 99 |
| abstract_inverted_index.involves | 156 |
| abstract_inverted_index.learning | 123, 127, 141 |
| abstract_inverted_index.patients | 39 |
| abstract_inverted_index.separate | 73 |
| abstract_inverted_index.severity | 16 |
| abstract_inverted_index.superior | 257 |
| abstract_inverted_index.utilized | 232 |
| abstract_inverted_index.visually | 29 |
| abstract_inverted_index.algorithm | 138 |
| abstract_inverted_index.automated | 49 |
| abstract_inverted_index.benchmark | 250 |
| abstract_inverted_index.dandelion | 136 |
| abstract_inverted_index.detection | 89 |
| abstract_inverted_index.diagnosis | 168 |
| abstract_inverted_index.examining | 30 |
| abstract_inverted_index.expensive | 47 |
| abstract_inverted_index.exploited | 199 |
| abstract_inverted_index.image.The | 63 |
| abstract_inverted_index.increased | 264 |
| abstract_inverted_index.introduce | 133 |
| abstract_inverted_index.presented | 153, 176 |
| abstract_inverted_index.technique | 155, 178, 262 |
| abstract_inverted_index.DOADL-BVSC | 154, 177, 245, 261 |
| abstract_inverted_index.accomplish | 173 |
| abstract_inverted_index.approaches | 110 |
| abstract_inverted_index.complexity | 6 |
| abstract_inverted_index.estimating | 26 |
| abstract_inverted_index.excitation | 213 |
| abstract_inverted_index.fundus.Due | 32 |
| abstract_inverted_index.generation | 221 |
| abstract_inverted_index.image.This | 82 |
| abstract_inverted_index.indicative | 106 |
| abstract_inverted_index.inspecting | 22 |
| abstract_inverted_index.physicians | 95 |
| abstract_inverted_index.simulation | 253 |
| abstract_inverted_index.stages.The | 240 |
| abstract_inverted_index.autoencoder | 228 |
| abstract_inverted_index.classifying | 236 |
| abstract_inverted_index.enhancement | 188 |
| abstract_inverted_index.grading.The | 152 |
| abstract_inverted_index.identifying | 234 |
| abstract_inverted_index.process.The | 48 |
| abstract_inverted_index.recognition | 55 |
| abstract_inverted_index.retinopathy | 1, 19 |
| abstract_inverted_index.surrounding | 78 |
| abstract_inverted_index.(DOADL-BVSC) | 148 |
| abstract_inverted_index.datasets.The | 252 |
| abstract_inverted_index.experimental | 241 |
| abstract_inverted_index.investigated | 248 |
| abstract_inverted_index.optimization | 137 |
| abstract_inverted_index.segmentation | 60, 66, 116, 145, 162 |
| abstract_inverted_index.process.Next, | 189 |
| abstract_inverted_index.techniques.In | 129 |
| abstract_inverted_index.Bi-directional | 192 |
| abstract_inverted_index.classification | 147, 165 |
| abstract_inverted_index.eye.Generally, | 11 |
| abstract_inverted_index.hyperparameter | 225 |
| abstract_inverted_index.photographs.To | 172 |
| abstract_inverted_index.classification, | 119 |
| abstract_inverted_index.ophthalmologist | 13 |
| abstract_inverted_index.disease.Numerous | 109 |
| abstract_inverted_index.optimizer.Finally, | 226 |
| abstract_inverted_index.effectively.Moreover, | 209 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.7399756 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |