Ensemble Learning in Systems of Neural Networks for Detection of Abnormal Shadows from X-ray Images of Lungs
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- Sasaki Takahiro
- Sharp Corporation Tottori University Electronic Display Research Center (TEDREC)
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- Kinoshita Kentaro
- Tottori University Electronic Display Research Center (TEDREC)
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- Kishida Satoru
- Tottori University Electronic Display Research Center (TEDREC)
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- Hirata Yoshiharu
- Tottori University Hospital
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- Yamada Seigo
- Tottori University Hospital
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説明
We clarified the effect of ensemble learning on the performance of systems with neural networks, using onedimensional numeric sequences as input patterns for the detection of abnormal shadows in X-ray images of lungs. In order to implement the ensemble learning, the input patterns, which were one-dimensional numeric sequences obtained from two-dimensional images, were preprocessed using several averaging and differential filters. Then, we combined several systems with neural networks constructed using the input patterns with different preprocessing conditions. From the results, we found that the ensemble learning improved the performance of the systems with neural networks using one-dimensional numeric sequences. The best value of areas under ROC curves in the systems with ensemble learning was superior to those in previous systems with twodimensional information as input patterns. Thus, the systems proposed in this study are thought to be useful for medical diagnosis.
収録刊行物
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- 信号処理
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信号処理 16 (4), 343-346, 2012
信号処理学会
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詳細情報 詳細情報について
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- CRID
- 1390001204464874112
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- NII論文ID
- 130004457029
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- ISSN
- 18801013
- 13426230
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可