Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model
Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the uri...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/7/1299 |
| _version_ | 1797608217215238144 |
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| author | Muhammed Yildirim Harun Bingol Emine Cengil Serpil Aslan Muhammet Baykara |
| author_facet | Muhammed Yildirim Harun Bingol Emine Cengil Serpil Aslan Muhammet Baykara |
| author_sort | Muhammed Yildirim |
| collection | DOAJ |
| description | Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model. |
| first_indexed | 2024-03-11T05:40:19Z |
| format | Article |
| id | doaj.art-ec3a1b7d5d1f4cb289f06d2a840be3bc |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T05:40:19Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-ec3a1b7d5d1f4cb289f06d2a840be3bc2023-11-17T16:30:38ZengMDPI AGDiagnostics2075-44182023-03-01137129910.3390/diagnostics13071299Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid ModelMuhammed Yildirim0Harun Bingol1Emine Cengil2Serpil Aslan3Muhammet Baykara4Department of Computer Engineering, Malatya Turgut Ozal University, Malatya 44200, TurkeyDepartment of Software Engineering, Malatya Turgut Ozal University, Malatya 44200, TurkeyDepartment of Computer Engineering, Bitlis Eren University, Bitlis 13100, TurkeyDepartment of Software Engineering, Malatya Turgut Ozal University, Malatya 44200, TurkeyDepartment of Software Engineering, Firat University, Elazig 23100, TurkeyUrine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.https://www.mdpi.com/2075-4418/13/7/1299classificationCNNkidneymRMRurine sediment |
| spellingShingle | Muhammed Yildirim Harun Bingol Emine Cengil Serpil Aslan Muhammet Baykara Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model Diagnostics classification CNN kidney mRMR urine sediment |
| title | Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model |
| title_full | Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model |
| title_fullStr | Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model |
| title_full_unstemmed | Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model |
| title_short | Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model |
| title_sort | automatic classification of particles in the urine sediment test with the developed artificial intelligence based hybrid model |
| topic | classification CNN kidney mRMR urine sediment |
| url | https://www.mdpi.com/2075-4418/13/7/1299 |
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