Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier
There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in...
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| Format: | Article |
| Language: | English |
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Sulaimani Polytechnic University
2023-01-01
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| Series: | Kurdistan Journal of Applied Research |
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| Online Access: | https://kjar.spu.edu.iq/index.php/kjar/article/view/794 |
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| _version_ | 1823861376815202304 |
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| author | Abdalbasit Mohammed Qadir Dana Faiq Abd |
| author_facet | Abdalbasit Mohammed Qadir Dana Faiq Abd |
| author_sort | Abdalbasit Mohammed Qadir |
| collection | DOAJ |
| description | There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent.
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| format | Article |
| id | doaj-art-eb5492399ce84b9eb4079fc74c78a6d1 |
| institution | Kabale University |
| issn | 2411-7684 2411-7706 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Sulaimani Polytechnic University |
| record_format | Article |
| series | Kurdistan Journal of Applied Research |
| spelling | doaj-art-eb5492399ce84b9eb4079fc74c78a6d12025-02-09T20:59:39ZengSulaimani Polytechnic UniversityKurdistan Journal of Applied Research2411-76842411-77062023-01-017210.24017/Science.2022.2.11Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest ClassifierAbdalbasit Mohammed Qadir0Dana Faiq Abd1Computer Science, College of Science and Technology ,University of Human Development, Sulaimani, IraqComputer Science, College of Science and Technology, University of Human Development, Sulaimani, IraqThere are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent. https://kjar.spu.edu.iq/index.php/kjar/article/view/794kidney DiseaseAI-Based SystemMachine LearningPre-Trained ModelFeature ExtractionClassification |
| spellingShingle | Abdalbasit Mohammed Qadir Dana Faiq Abd Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier Kurdistan Journal of Applied Research kidney Disease AI-Based System Machine Learning Pre-Trained Model Feature Extraction Classification |
| title | Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier |
| title_full | Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier |
| title_fullStr | Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier |
| title_full_unstemmed | Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier |
| title_short | Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier |
| title_sort | kidney diseases classification using hybrid transfer learning densenet201 based and random forest classifier |
| topic | kidney Disease AI-Based System Machine Learning Pre-Trained Model Feature Extraction Classification |
| url | https://kjar.spu.edu.iq/index.php/kjar/article/view/794 |
| work_keys_str_mv | AT abdalbasitmohammedqadir kidneydiseasesclassificationusinghybridtransferlearningdensenet201basedandrandomforestclassifier AT danafaiqabd kidneydiseasesclassificationusinghybridtransferlearningdensenet201basedandrandomforestclassifier |