Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features
Nephrolithiasis is a scientific term that refers to kidney stones and means the formation of crystal concretions in the kidney. It is considered a widespread situation that affects millions of people worldwide. Those stones can cause serious discomfort to infected people, especially when they traver...
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University of Human Development
2025-01-01
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| Series: | UHD Journal of Science and Technology |
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| Online Access: | https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1355 |
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| author | Hawkar K. Hama Hamsa D. Majeed Goran Saman Nariman |
| author_facet | Hawkar K. Hama Hamsa D. Majeed Goran Saman Nariman |
| author_sort | Hawkar K. Hama |
| collection | DOAJ |
| description | Nephrolithiasis is a scientific term that refers to kidney stones and means the formation of crystal concretions in the kidney. It is considered a widespread situation that affects millions of people worldwide. Those stones can cause serious discomfort to infected people, especially when they traverse the urinary system, although, the big stones may need a surgical intervention. Various systems are already in use to address kidney stones, including ultrasound imaging for detection, extracorporeal shock wave lithotripsy (ESWL) for non-invasive stone fragmentation, and ureteroscopy for surgical removal, showcasing the advances in medical technology for managing this condition. This study presents an approach for detecting stones in the affected kidney. A public dataset has been employed in this work, containing (2370) images of healthy and affected kidneys. The dataset was utilized to train the proposed approach for the aim of stone detection. To achieve high detection accuracy, we implemented two key phases before classification. The preprocessing phase enhances image quality by reducing noise using a median filter and improving contrast through contrast stretching and tone enhancement. The segmentation phase follows, accurately identifying the kidney’s edges and regions of interest for effective feature extraction. The Local Binary Pattern (LBP) technique, combined with the support vector machine (SVM) algorithm serves as the primary components of the proposed model. The feature extraction comes into action through the LBP technique as a preparation step for the SVM classifier to complete the stone detection process. The approach introduced in this paper has the potential to enhance detection accuracy and efficiency. Furthermore, it could be used as an early detection tool to identify potential cases, thereby helping to prevent complications and adverse outcomes. This method aims to improve on the traditional manual process employed by radiologists, which could be described as time and effort consumption rather than the exposure of the interpretations. The obtained results were compared with the most relevant approaches in the field of kidney stone detection, demonstrating the model’s effectiveness in achieving the desired goal with a diagnostic accuracy of 96.37% for kidney stones. |
| format | Article |
| id | doaj-art-c7661d90f43149ae96d8a6d0b5de90f6 |
| institution | DOAJ |
| issn | 2521-4209 2521-4217 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | University of Human Development |
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| series | UHD Journal of Science and Technology |
| spelling | doaj-art-c7661d90f43149ae96d8a6d0b5de90f62025-08-20T03:06:08ZengUniversity of Human DevelopmentUHD Journal of Science and Technology2521-42092521-42172025-01-0191101710.21928/uhdjst.v9n1y2025.pp10-171488Enhanced Kidney Stone Detection and Classification Using SVM and LBP FeaturesHawkar K. Hama0Hamsa D. Majeed1Goran Saman Nariman2Department of Computer Science, College of Basic Education, University of Sulaimani, Kurdistan region, IraqDepartment of Information Technology, College of Science and Technology, University of Human Development, Kurdistan Region, IraqDepartment of Information Technology, College of Science and Technology, University of Human Development, Kurdistan Region, IraqNephrolithiasis is a scientific term that refers to kidney stones and means the formation of crystal concretions in the kidney. It is considered a widespread situation that affects millions of people worldwide. Those stones can cause serious discomfort to infected people, especially when they traverse the urinary system, although, the big stones may need a surgical intervention. Various systems are already in use to address kidney stones, including ultrasound imaging for detection, extracorporeal shock wave lithotripsy (ESWL) for non-invasive stone fragmentation, and ureteroscopy for surgical removal, showcasing the advances in medical technology for managing this condition. This study presents an approach for detecting stones in the affected kidney. A public dataset has been employed in this work, containing (2370) images of healthy and affected kidneys. The dataset was utilized to train the proposed approach for the aim of stone detection. To achieve high detection accuracy, we implemented two key phases before classification. The preprocessing phase enhances image quality by reducing noise using a median filter and improving contrast through contrast stretching and tone enhancement. The segmentation phase follows, accurately identifying the kidney’s edges and regions of interest for effective feature extraction. The Local Binary Pattern (LBP) technique, combined with the support vector machine (SVM) algorithm serves as the primary components of the proposed model. The feature extraction comes into action through the LBP technique as a preparation step for the SVM classifier to complete the stone detection process. The approach introduced in this paper has the potential to enhance detection accuracy and efficiency. Furthermore, it could be used as an early detection tool to identify potential cases, thereby helping to prevent complications and adverse outcomes. This method aims to improve on the traditional manual process employed by radiologists, which could be described as time and effort consumption rather than the exposure of the interpretations. The obtained results were compared with the most relevant approaches in the field of kidney stone detection, demonstrating the model’s effectiveness in achieving the desired goal with a diagnostic accuracy of 96.37% for kidney stones.https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1355medical image analysisct imageslocal binary patternsupport vector machinekidney stones |
| spellingShingle | Hawkar K. Hama Hamsa D. Majeed Goran Saman Nariman Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features UHD Journal of Science and Technology medical image analysis ct images local binary pattern support vector machine kidney stones |
| title | Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features |
| title_full | Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features |
| title_fullStr | Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features |
| title_full_unstemmed | Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features |
| title_short | Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features |
| title_sort | enhanced kidney stone detection and classification using svm and lbp features |
| topic | medical image analysis ct images local binary pattern support vector machine kidney stones |
| url | https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1355 |
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