Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease
Artificial Neural Networks are excellent at identifying patterns or trends in data, which makes them perfect for forecasting or prediction. Thus, neural networks have extensive application in biological systems. The application of neural networks to kidney stone diagnosis is emphasized in this arti...
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| Format: | Article |
| Language: | English |
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International Transactions on Electrical Engineering and Computer Science
2024-03-01
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| Series: | International Transactions on Electrical Engineering and Computer Science |
| Subjects: | |
| Online Access: | https://iteecs.com/index.php/iteecs/article/view/74 |
| _version_ | 1797228522095247360 |
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| author | Dushyanth Kumar Reena Rani Navneet Vivek Nitesh Kumar |
| author_facet | Dushyanth Kumar Reena Rani Navneet Vivek Nitesh Kumar |
| author_sort | Dushyanth Kumar |
| collection | DOAJ |
| description |
Artificial Neural Networks are excellent at identifying patterns or trends in data, which makes them perfect for forecasting or prediction. Thus, neural networks have extensive application in biological systems. The application of neural networks to kidney stone diagnosis is emphasized in this article. Kidney stone issues can be diagnosed with neural networks by applying technological concepts such as MLP, SVM, RBF, and BPA. The purpose of this research is to use three different neural network algorithms—each with its own specific design and set of properties to identify kidney stone disease. The performance of the three neural networks is compared in this research with respect to training data set size, model creation time, and accuracy. Kidney stone sickness will be diagnosed using radial basis function (RBF) networks, two layers feed forward perceptrons trained with the back propagation training algorithm, and learning vector quantization (LVQ). However, determining the best approach for any particular diagnostic had never been an easy task. Like many other illnesses, kidney stones have already been diagnosed using neural network algorithms. The main objective of this work is to recommend the best medical diagnostic instrument, such as kidney stone detection, to reduce diagnosis times and improve accuracy and efficiency.
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| first_indexed | 2024-04-24T14:58:02Z |
| format | Article |
| id | doaj.art-adebbbabe37e43369cbea7509eec5b6a |
| institution | Directory Open Access Journal |
| issn | 2583-6471 |
| language | English |
| last_indexed | 2024-04-24T14:58:02Z |
| publishDate | 2024-03-01 |
| publisher | International Transactions on Electrical Engineering and Computer Science |
| record_format | Article |
| series | International Transactions on Electrical Engineering and Computer Science |
| spelling | doaj.art-adebbbabe37e43369cbea7509eec5b6a2024-04-02T17:30:46ZengInternational Transactions on Electrical Engineering and Computer ScienceInternational Transactions on Electrical Engineering and Computer Science2583-64712024-03-013110.62760/iteecs.3.1.2024.74Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone diseaseDushyanth Kumar0https://orcid.org/0009-0002-8128-8941Reena Rani1Navneet Vivek2Nitesh Kumar3Department of Electronics and Communication Engineering, Dev Bhoomi Group of Institutions, Saharnarpur - 247001, India.Department of Electronics and Communication Engineering, IMS Engineering College, Ghaziabhad-201015, IndiaDepartment of Electronics and Communication Engineering, COER University, Roorkee- 247667, IndiaDepartment of Electronics and Communication Engineering, Roorkee Institute of Technology, Roorkee - 247668, India Artificial Neural Networks are excellent at identifying patterns or trends in data, which makes them perfect for forecasting or prediction. Thus, neural networks have extensive application in biological systems. The application of neural networks to kidney stone diagnosis is emphasized in this article. Kidney stone issues can be diagnosed with neural networks by applying technological concepts such as MLP, SVM, RBF, and BPA. The purpose of this research is to use three different neural network algorithms—each with its own specific design and set of properties to identify kidney stone disease. The performance of the three neural networks is compared in this research with respect to training data set size, model creation time, and accuracy. Kidney stone sickness will be diagnosed using radial basis function (RBF) networks, two layers feed forward perceptrons trained with the back propagation training algorithm, and learning vector quantization (LVQ). However, determining the best approach for any particular diagnostic had never been an easy task. Like many other illnesses, kidney stones have already been diagnosed using neural network algorithms. The main objective of this work is to recommend the best medical diagnostic instrument, such as kidney stone detection, to reduce diagnosis times and improve accuracy and efficiency. https://iteecs.com/index.php/iteecs/article/view/74Kidney stoneArtificial neural networkRadial basis functionLearning vector quantization |
| spellingShingle | Dushyanth Kumar Reena Rani Navneet Vivek Nitesh Kumar Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease International Transactions on Electrical Engineering and Computer Science Kidney stone Artificial neural network Radial basis function Learning vector quantization |
| title | Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease |
| title_full | Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease |
| title_fullStr | Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease |
| title_full_unstemmed | Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease |
| title_short | Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease |
| title_sort | compare the performance of distinct neural networks techniques to diagnose the kidney stone disease |
| topic | Kidney stone Artificial neural network Radial basis function Learning vector quantization |
| url | https://iteecs.com/index.php/iteecs/article/view/74 |
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