Neural network approach for differential diagnosis of interstitial lung diseases

Naoki Asada; Kunio Doi; Heber MacMahon; Steven M. Montner; Maryellen Lissak Giger; Chihiro Abe; Chris Yuzheng Wu

doi:10.1117/12.18887

1 July 1990 Neural network approach for differential diagnosis of interstitial lung diseases

Naoki Asada, Kunio Doi, Heber MacMahon, Steven M. Montner, Maryellen Lissak Giger, Chihiro Abe, Chris Yuzheng Wu

Proceedings Volume 1233, Medical Imaging IV: Image Processing; (1990) https://doi.org/10.1117/12.18887
Event: Medical Imaging '90, 1990, Newport Beach, CA, United States

Abstract

A neural network approach was applied for the differential diagnosis of interstitial lung diseases. The neural network was designed for distinguishing between 9 types of interstitial lung diseases based on 20 items of clinical and radiographic information. A database for training and testing the neural network was created with 10 hypothetical cases for each of the 9 diseases. The performance of the neural network was evaluated by ROC analysis. The optimal parameters for the current neural network were determined by selecting those yielding the highest ROC curves. In this case the neural network consisted of one hidden layer including 6 units and was trained with 200 learning iterations. When the decision performances of the neural network chest radiologists and senior radiology residents were compared the neural network indicated high performance comparable to that of chest radiologists and superior to that of senior radiology residents. Our preliminary results suggested strongly that the neural network approach had potential utility in the computer-aided differential diagnosis of interstitial lung diseases. 1_

Citation Download Citation

Naoki Asada, Kunio Doi, Heber MacMahon, Steven M. Montner, Maryellen Lissak Giger, Chihiro Abe, and Chris Yuzheng Wu "Neural network approach for differential diagnosis of interstitial lung diseases", Proc. SPIE 1233, Medical Imaging IV: Image Processing, (1 July 1990); https://doi.org/10.1117/12.18887

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