@techreport{oai:ipsj.ixsq.nii.ac.jp:00069784,
 author = {Yuki, Tanaka and Hayaru, Shouno and Shoji, Kido and Yuki, Tanaka and Hayaru, Shouno and Shoji, Kido},
 issue = {7},
 month = {Jul},
 note = {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.},
 title = {Classification of Idiopathic Interstitial Pneumonia on High-resolution CT Images using Counter-propagation Network},
 year = {2010}
}