Automatic Kidney Stone Detection System using Guided Bilateral Feature Detector for CT Images Article Swipe
R Manoranjitham
,
S. Punitha
,
Vinayakumar Ravi
,
Thompson Stephan
,
Alanoud Al Mazroa
,
Prabhishek Singh
,
Manoj Diwakar
,
Indrajeet Gupta
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2174/0118749445334602240820074311
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2174/0118749445334602240820074311
Background Kidney stones, common urological diseases worldwide, are formed from hard urine minerals in the kidneys. Early detection is essential to prevent kidney damage and manage recurring stones. CT imaging has made significant progress in providing detailed information for disease diagnosis. Aim This study aimed to enhance kidney stone detection through advanced imaging and machine learning techniques. Objective The Guided Bilateral Feature Detector was proposed to identify and extract features for kidney stone detection in CT images. Unlike traditional filters like Gaussian and Bilateral filters, the Guided Bilateral Filter Detector prevented halo artifacts and preserved image edges by employing a guide weight. The extracted features were combined with the SVM algorithm to accurately detect kidney stones in CT images. Methods The proposed detector used the Guided Bilateral Filter to reduce the halo artifacts in the images and enhance the feature detection process. The detector operated in four stages to extract important features from CT images, and a 128-feature point generator provided a more detailed representation in aiding kidney stone detection and classification. The proposed detector combined with the Support Vector Machine algorithm to improve reliability and reduce computational requirements. Results Experimental results showed that the proposed Guided Bilateral Feature Detector with SVM outperformed existing models, including SIFT+SVM, SURF+SVM, PCA+KNN, EANet, Inception v3, VGG16, and Resnet50. The key performance metrics achieved included an accuracy of 98.56%, precision of 98.9%, recall of 99.2%, and an F1 score of 99%. Conclusion The findings indicate that the Guided Bilateral Feature Detector with SVM significantly enhances the accuracy and reliability of kidney stone detection, providing valuable implications for clinical practice and future research in medical imaging.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2174/0118749445334602240820074311
- OA Status
- diamond
- Cited By
- 2
- References
- 20
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403134376Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2174/0118749445334602240820074311Digital Object Identifier
- Title
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Automatic Kidney Stone Detection System using Guided Bilateral Feature Detector for CT ImagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-10-04Full publication date if available
- Authors
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R Manoranjitham, S. Punitha, Vinayakumar Ravi, Thompson Stephan, Alanoud Al Mazroa, Prabhishek Singh, Manoj Diwakar, Indrajeet GuptaList of authors in order
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https://doi.org/10.2174/0118749445334602240820074311Publisher landing page
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diamondOpen access status per OpenAlex
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https://doi.org/10.2174/0118749445334602240820074311Direct OA link when available
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Feature (linguistics), Kidney stones, Computer vision, Detector, Artificial intelligence, Computer science, Biomedical engineering, Radiology, Nuclear medicine, Medicine, Surgery, Linguistics, Philosophy, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Machine | 180 |
| abstract_inverted_index.Methods | 119 |
| abstract_inverted_index.Results | 189 |
| abstract_inverted_index.Support | 178 |
| abstract_inverted_index.disease | 39 |
| abstract_inverted_index.enhance | 46, 137 |
| abstract_inverted_index.extract | 68, 149 |
| abstract_inverted_index.feature | 139 |
| abstract_inverted_index.filters | 79 |
| abstract_inverted_index.images, | 154 |
| abstract_inverted_index.images. | 76, 118 |
| abstract_inverted_index.imaging | 29, 52 |
| abstract_inverted_index.improve | 183 |
| abstract_inverted_index.machine | 54 |
| abstract_inverted_index.medical | 269 |
| abstract_inverted_index.metrics | 218 |
| abstract_inverted_index.models, | 204 |
| abstract_inverted_index.prevent | 21 |
| abstract_inverted_index.results | 191 |
| abstract_inverted_index.stones, | 2 |
| abstract_inverted_index.stones. | 27 |
| abstract_inverted_index.through | 50 |
| abstract_inverted_index.weight. | 101 |
| abstract_inverted_index.Detector | 62, 89, 199, 246 |
| abstract_inverted_index.Gaussian | 81 |
| abstract_inverted_index.PCA+KNN, | 208 |
| abstract_inverted_index.accuracy | 222, 252 |
| abstract_inverted_index.achieved | 219 |
| abstract_inverted_index.advanced | 51 |
| abstract_inverted_index.clinical | 263 |
| abstract_inverted_index.combined | 106, 175 |
| abstract_inverted_index.detailed | 36, 163 |
| abstract_inverted_index.detector | 122, 143, 174 |
| abstract_inverted_index.diseases | 5 |
| abstract_inverted_index.enhances | 250 |
| abstract_inverted_index.existing | 203 |
| abstract_inverted_index.features | 69, 104, 151 |
| abstract_inverted_index.filters, | 84 |
| abstract_inverted_index.findings | 239 |
| abstract_inverted_index.identify | 66 |
| abstract_inverted_index.imaging. | 270 |
| abstract_inverted_index.included | 220 |
| abstract_inverted_index.indicate | 240 |
| abstract_inverted_index.kidneys. | 15 |
| abstract_inverted_index.learning | 55 |
| abstract_inverted_index.minerals | 12 |
| abstract_inverted_index.operated | 144 |
| abstract_inverted_index.practice | 264 |
| abstract_inverted_index.process. | 141 |
| abstract_inverted_index.progress | 33 |
| abstract_inverted_index.proposed | 64, 121, 173, 195 |
| abstract_inverted_index.provided | 160 |
| abstract_inverted_index.research | 267 |
| abstract_inverted_index.valuable | 260 |
| abstract_inverted_index.Bilateral | 60, 83, 87, 126, 197, 244 |
| abstract_inverted_index.Inception | 210 |
| abstract_inverted_index.Objective | 57 |
| abstract_inverted_index.Resnet50. | 214 |
| abstract_inverted_index.SIFT+SVM, | 206 |
| abstract_inverted_index.SURF+SVM, | 207 |
| abstract_inverted_index.algorithm | 110, 181 |
| abstract_inverted_index.artifacts | 92, 132 |
| abstract_inverted_index.detection | 17, 49, 73, 140, 169 |
| abstract_inverted_index.employing | 98 |
| abstract_inverted_index.essential | 19 |
| abstract_inverted_index.extracted | 103 |
| abstract_inverted_index.generator | 159 |
| abstract_inverted_index.important | 150 |
| abstract_inverted_index.including | 205 |
| abstract_inverted_index.precision | 225 |
| abstract_inverted_index.preserved | 94 |
| abstract_inverted_index.prevented | 90 |
| abstract_inverted_index.providing | 35, 259 |
| abstract_inverted_index.recurring | 26 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 237 |
| abstract_inverted_index.accurately | 112 |
| abstract_inverted_index.detection, | 258 |
| abstract_inverted_index.diagnosis. | 40 |
| abstract_inverted_index.urological | 4 |
| abstract_inverted_index.worldwide, | 6 |
| abstract_inverted_index.128-feature | 157 |
| abstract_inverted_index.information | 37 |
| abstract_inverted_index.performance | 217 |
| abstract_inverted_index.reliability | 184, 254 |
| abstract_inverted_index.significant | 32 |
| abstract_inverted_index.techniques. | 56 |
| abstract_inverted_index.traditional | 78 |
| abstract_inverted_index.Experimental | 190 |
| abstract_inverted_index.implications | 261 |
| abstract_inverted_index.outperformed | 202 |
| abstract_inverted_index.computational | 187 |
| abstract_inverted_index.requirements. | 188 |
| abstract_inverted_index.significantly | 249 |
| abstract_inverted_index.representation | 164 |
| abstract_inverted_index.classification. | 171 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.63329015 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |