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最適なパラメータチューニングによる尿沈渣結晶の深層学習を用いた分類
http://hdl.handle.net/10191/0002001062
http://hdl.handle.net/10191/00020010622b6966f5-61d6-4d91-8b9c-0ece311205b3
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||||||
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| 公開日 | 2023-07-26 | |||||||||
| タイトル | ||||||||||
| タイトル | Deep learning classification of urinary sediment crystals with optimal parameter tuning | |||||||||
| 言語 | en | |||||||||
| タイトル | ||||||||||
| タイトル | 最適なパラメータチューニングによる尿沈渣結晶の深層学習を用いた分類 | |||||||||
| 言語 | ja | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||||
| 資源タイプ | doctoral thesis | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
| 著者 |
永井, 貴大
× 永井, 貴大
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | The examination of urinary sediment crystals, the sedimentary components of urine, is useful in screening tests, and is always performed in medical examinations. The examination of urinary sediment crystals is typically done by classifying them under a microscope. Although automated analyzers are commercially available, manual classification is required, which is time-consuming and varies depending on the technologist performing the test and the laboratory. A set of test images was created, consisting of training, validation, and test images. The training images were transformed and augmented using various methods. The test images were classified to determine the patterns that could be correctly classified. Convolutional neural networks were used for training. Furthermore, we also considered the case where the crystal subcategories were not treated as separate. Learning with all parameters except the random cropping parameter showed the highest accuracy value. Treating the subcategories together or separately did not seem to affect the accuracy value. The accuracy of the best pattern was 0.918. When matched to a real-world case, the percentage of correct answers was 88%. Although the number of images was limited, good results were obtained in the classification of crystal images with optimal parameter tuning. The parameter optimization performed in this study can be used as a reference for future studies, with the goal of image classification by deep learning in clinical practice. | |||||||||
| 言語 | en | |||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Scientific Reports. 2022, 12, 21178. | |||||||||
| 言語 | en | |||||||||
| DOI | ||||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1038/s41598-022-25385-x | |||||||||
| 権利 | ||||||||||
| 言語 | en | |||||||||
| 権利情報 | © The Author(s) 2022 | |||||||||
| 権利 | ||||||||||
| 言語 | en | |||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | Creative Commons Attribution 4.0 International | |||||||||
| 学位名 | ||||||||||
| 言語 | ja | |||||||||
| 学位名 | 博士（医学） | |||||||||
| 学位授与機関 | ||||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||||
| 学位授与機関識別子 | 13101 | |||||||||
| 言語 | ja | |||||||||
| 学位授与機関名 | 新潟大学 | |||||||||
| 言語 | en | |||||||||
| 学位授与機関名 | Niigata University | |||||||||
| 学位授与年月日 | ||||||||||
| 学位授与年月日 | 2023-03-23 | |||||||||
| 学位授与番号 | ||||||||||
| 学位授与番号 | 甲第5124号 | |||||||||
| 学位記番号 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 新大院博(医)第1115号 | |||||||||
| 言語 | ja | |||||||||
