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.
authors.affiliation: Department of Radiology, Iwate Medical University, Morioka, Japan.
authors.forename: S
authors.initials: S
authors.lastname: Katsuragawa
authors.forename: K
authors.initials: K
authors.lastname: Doi
authors.forename: H
authors.initials: H
authors.lastname: MacMahon
authors.forename: L
authors.initials: L
authors.lastname: Monnier-Cholley
authors.forename: T
authors.initials: T
authors.lastname: Ishida
authors.forename: T
authors.initials: T
authors.lastname: Kobayashi
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods; D001330:Electronic Data Processing / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D017563:Lung Diseases, Interstitial / Q000145:classification* / Q000000981:diagnostic imaging; D016571:Neural Networks, Computer; D013902:Radiography, Thoracic / Q000379:methods*; D012680:Sensitivity and Specificity