Classification of Idiopathic Interstitial Pneumonia on High-resolution CT Images using Counter-propagation Network

• Item type: SIG Technical Reports(1)
• 公開日: 2010-07-05
• タイトル: Classification of Idiopathic Interstitial Pneumonia on High-resolution CT Images using Counter-propagation Network
• タイトル / 言語: en
• タイトル: Classification of Idiopathic Interstitial Pneumonia on High-resolution CT Images using Counter-propagation Network
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Applied Medical Engineering Sciece, Graduate School of Medicine, Yamaguchi University
• 著者所属: Graduate School of Informatics and Engineering, University of Electro-Communications
• 著者所属: Applied Medical Engineering Sciece, Graduate School of Medicine, Yamaguchi University
• 著者所属(英): en
• 著者所属(英): Applied Medical Engineering Sciece, Graduate School of Medicine, Yamaguchi University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics and Engineering, University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): Applied Medical Engineering Sciece, Graduate School of Medicine, Yamaguchi University
• 著者名: Yuki, Tanaka; Hayaru, Shouno; Shoji, Kido
• 著者名(英): Yuki, Tanaka; Hayaru, Shouno; Shoji, Kido
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In order to classify the idiopathic interstitial pneumonias(IIPs), extraction and interpretation of features on high-resolution computed tomography (HRCT) image is considered to be effective. The purpose of our study is to develop a diagnosis support system to help diagnostician of classification for those HRCT images using an artificial neural network called counter propagation network. The CPN is a hybrid type neural network model composed from self-organizing map (SOM) for feature extraction and from multi-layered perceptron (MLP) for classification. Applying the CPN for the IIPs images, we could obtain both a kind of similarity map and classification system.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In order to classify the idiopathic interstitial pneumonias(IIPs), extraction and interpretation of features on high-resolution computed tomography (HRCT) image is considered to be effective. The purpose of our study is to develop a diagnosis support system to help diagnostician of classification for those HRCT images using an artificial neural network called counter propagation network. The CPN is a hybrid type neural network model composed from self-organizing map (SOM) for feature extraction and from multi-layered perceptron (MLP) for classification. Applying the CPN for the IIPs images, we could obtain both a kind of similarity map and classification system.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2010-MPS-79, 号 7, p. 1-6, 発行日 2010-07-05
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会