title: Neural network and conventional classifiers for fluorescence-guided laser angioplasty.
abstract: In laser angioplasty, fluorescence spectra of targeted tissue may be used to classify the tissue as atherosclerotic or normal and guide selective laser ablation of atherosclerotic plaque. Here, the ability of the back-propagation and K-nearest neighbors techniques to classify arterial fluorescence spectra is investigated. Both methods are competitive with other classification schemes. The relative performance of variations on both techniques is used to make inferences about the geometry of the classification task.
authors.affiliation: Department of Diagnostic Radiology, Yale University, New Haven, CT 06510.
authors.forename: G R
authors.initials: GR
authors.lastname: Gindi
authors.forename: C J
authors.initials: CJ
authors.lastname: Darken
authors.forename: K M
authors.initials: KM
authors.lastname: O'Brien
authors.forename: M L
authors.initials: ML
authors.lastname: Stetz
authors.forename: L I
authors.initials: LI
authors.lastname: Deckelbaum
mesh_terms: D016342:Angioplasty, Laser; D001161:Arteriosclerosis / Q000145:classification* / Q000601:surgery; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D005453:Fluorescence; D006801:Humans; D010363:Pattern Recognition, Automated