2026-04-20T22:43:35Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00069784 2025-01-21T23:46:43Z 1164:2735:6033:6124

Classification of Idiopathic Interstitial Pneumonia on High-resolution CT Images using Counter-propagation Network Classification of Idiopathic Interstitial Pneumonia on High-resolution CT Images using Counter-propagation Network Yuki, Tanaka Hayaru, Shouno Shoji, Kido Yuki, Tanaka Hayaru, Shouno Shoji, Kido 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. 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. 情報処理学会 2010-07-05 eng technical report https://ipsj.ixsq.nii.ac.jp/records/69784 AN10505667 研究報告数理モデル化と問題解決（MPS） 2010-MPS-79 7 1 6 https://ipsj.ixsq.nii.ac.jp/record/69784/files/IPSJ-MPS10079007.pdf application/pdf 1.8 MB 2012-07-05