Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.
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 im...
| Publicado en: | Journal of Digital Imaging Vol. 30; no. 4; pp. 413 - 427 |
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| Autores principales: | , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124395639&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124395639 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2017 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124395639 124395639 143944466 124395639 10.1007/s10278-017-9942-0 124395639 ppf: 413 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages. aug: au: Okumura, Eiichiro Kawashita, Ikuo Ishida, Takayuki affil: Department of Clinical Radiology, Faculty of Health Sciences , Tsukuba International University , 6-20-1, Manabe, Tsuchiura Ibaraki 300-0051 Japan sug: subj: Pneumoconiosis Classification Neural Networks (Computer) Radiography, Thoracic Diagnosis, Computer Assisted Human ROC Curve Image Interpretation, Computer Assisted ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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