Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification
The rising global incidence of chronic kidney disease necessitates the development of image categorization of renal glomeruli. COVID-19 has been shown to enter the glomerulus, a tissue structure in the kidney. This study observes the differences between focal-segmental, normal and sclerotic renal gl...
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MDPI AG
2022-01-01
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Online Access: | https://www.mdpi.com/2076-3417/12/3/1040 |
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author | Hsi-Chieh Lee Ahmad Fauzan Aqil |
author_facet | Hsi-Chieh Lee Ahmad Fauzan Aqil |
author_sort | Hsi-Chieh Lee |
collection | DOAJ |
description | The rising global incidence of chronic kidney disease necessitates the development of image categorization of renal glomeruli. COVID-19 has been shown to enter the glomerulus, a tissue structure in the kidney. This study observes the differences between focal-segmental, normal and sclerotic renal glomerular tissue diseases. The splitting and combining of allied and multivariate models was accomplished utilizing a combined technique using existing models. In this study, model combinations are created by using a high-accuracy accuracy-based model to improve other models. This research exhibits excellent accuracy and consistent classification results on the ResNet101V2 combination using a mix of transfer learning methods, with the combined model on ResNet101V2 showing an accuracy of up to 97 percent with an F1-score of 0.97, compared to other models. However, this study discovered that the anticipated time required was higher than the model employed in general, which was mitigated by the usage of high-performance computing in this study. |
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institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-03-10T00:16:07Z |
publishDate | 2022-01-01 |
publisher | MDPI AG |
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spelling | doaj.art-99cee985d5b04a6482aa04309af41ef12023-11-23T15:50:53ZengMDPI AGApplied Sciences2076-34172022-01-01123104010.3390/app12031040Combination of Transfer Learning Methods for Kidney Glomeruli Image ClassificationHsi-Chieh Lee0Ahmad Fauzan Aqil1Department of Computer Science and Information Engineering, National Quemoy University, Kinmen 89250, TaiwanDepartment of Computer Science and Information Engineering, National Quemoy University, Kinmen 89250, TaiwanThe rising global incidence of chronic kidney disease necessitates the development of image categorization of renal glomeruli. COVID-19 has been shown to enter the glomerulus, a tissue structure in the kidney. This study observes the differences between focal-segmental, normal and sclerotic renal glomerular tissue diseases. The splitting and combining of allied and multivariate models was accomplished utilizing a combined technique using existing models. In this study, model combinations are created by using a high-accuracy accuracy-based model to improve other models. This research exhibits excellent accuracy and consistent classification results on the ResNet101V2 combination using a mix of transfer learning methods, with the combined model on ResNet101V2 showing an accuracy of up to 97 percent with an F1-score of 0.97, compared to other models. However, this study discovered that the anticipated time required was higher than the model employed in general, which was mitigated by the usage of high-performance computing in this study.https://www.mdpi.com/2076-3417/12/3/1040combined classification modeldeep transfer learningfocal-segmentalkidney diseasekidney glomerulimedical image |
spellingShingle | Hsi-Chieh Lee Ahmad Fauzan Aqil Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification Applied Sciences combined classification model deep transfer learning focal-segmental kidney disease kidney glomeruli medical image |
title | Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification |
title_full | Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification |
title_fullStr | Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification |
title_full_unstemmed | Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification |
title_short | Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification |
title_sort | combination of transfer learning methods for kidney glomeruli image classification |
topic | combined classification model deep transfer learning focal-segmental kidney disease kidney glomeruli medical image |
url | https://www.mdpi.com/2076-3417/12/3/1040 |
work_keys_str_mv | AT hsichiehlee combinationoftransferlearningmethodsforkidneyglomeruliimageclassification AT ahmadfauzanaqil combinationoftransferlearningmethodsforkidneyglomeruliimageclassification |