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Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages

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Abstract

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

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Authors and Affiliations

  1. Department of Clinical Radiology, Faculty of Health Sciences, Tsukuba International University, 6-20-1, Manabe, Tsuchiura, Ibaraki, 300-0051, Japan

    Eiichiro Okumura

  2. Department of Clinical Radiology, Hiroshima International University, 555-36, kurosegakuendai, Higashihiroshima, Hiroshima, 739-2695, Japan

    Ikuo Kawashita

  3. Division of Health Sciences, Graduate School of Medicine, Osaka University, 1-7, Yamadaoka, Suita, 565-0871, Japan

    Takayuki Ishida

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  1. Eiichiro Okumura
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  3. Takayuki Ishida
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Correspondence to Eiichiro Okumura.

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Okumura, E., Kawashita, I. & Ishida, T. Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages. J Digit Imaging 30, 413–426 (2017). https://doi.org/10.1007/s10278-017-9942-0

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