Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification
| Autor: | Ahmad Fauzan Aqil, Hsi-Chieh Lee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Fluid Flow and Transfer Processes
Technology QH301-705.5 Process Chemistry and Technology kidney disease Physics QC1-999 General Engineering combined classification model Engineering (General). Civil engineering (General) focal-segmental Computer Science Applications medical image Chemistry deep transfer learning General Materials Science TA1-2040 Biology (General) Instrumentation kidney glomeruli sclerosed glomeruli QD1-999 |
| Zdroj: | Applied Sciences, Vol 12, Iss 1040, p 1040 (2022) Applied Sciences; Volume 12; Issue 3; Pages: 1040 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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