Paper Info
Title
Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.
Abstract
We devised an automated classification scheme by using the rule-based method plus artificial neural networks (ANN) for distinction between normal and abnormal lungs with interstitial disease in digital chest radiographs. Four measures used in the classification scheme are determined from the texture and geometric-pattern feature analyses. The rms variation and the first moment of the power spectrum of lung patterns are determined as measures for the texture analysis. In addition, the total area of nodular opacities and the total length of linear opacities are determined as measures for the geometric-pattern feature analysis. In our classification scheme with these measures, we identify obviously normal and abnormal cases first by the rule-based method and then ANN is applied for the remaining difficult cases. The rule-based plus ANN method provided a sensitivity of 0.926 at the specificity of 0.900, which was considerably improved compared to performance of either the rule-based method alone or ANNs alone.
Year
DOI
Venue
1997
10.1007/BF03168597
J. Digital Imaging
Keywords
Field
DocType
computer-aided diagnosis,interstitial lung disease,automated classification,chest radiography
Computer vision,Rule-based system,Pattern recognition,Computer science,Classification scheme,Computer-aided diagnosis,Moment (mathematics),Artificial intelligence,Artificial neural network,Pattern recognition (psychology)
Journal
Volume
Issue
ISSN
10
3
0897-1889
Citations 
PageRank 
References 
6
0.83
2
Authors
6
Name
Order
Citations
PageRank
Shigehiko Katsuragawa117226.20
K Doi213526.74
Heber MacMahon320231.61
L Monnier-Cholley4132.73
Takayuki Ishida55612.36
Takao Kobayashi61322125.24