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dc.contributor.authorSahiner, Berkmanen_US
dc.contributor.authorChan, Heang-Pingen_US
dc.contributor.authorPetrick, Nicholas A.en_US
dc.contributor.authorHelvie, Mark A.en_US
dc.contributor.authorGoodsitt, Mitchell M.en_US
dc.date.accessioned2006-12-19T19:02:58Z
dc.date.available2006-12-19T19:02:58Z
dc.date.issued1998-10-01en_US
dc.identifier.citationSahiner, Berkman; Chan, Heang-Ping; Petrick, Nicholas; Helvie, Mark A; Goodsitt, Mitchell M (1998). "Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis ." Physics in Medicine and Biology. 43(10): 2853-2871. <http://hdl.handle.net/2027.42/48962>en_US
dc.identifier.issn0031-9155en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/48962
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=9814523&dopt=citationen_US
dc.description.abstractA genetic algorithm (GA) based feature selection method was developed for the design of high-sensitivity classifiers, which were tailored to yield high sensitivity with high specificity. The fitness function of the GA was based on the receiver operating characteristic (ROC) partial area index, which is defined as the average specificity above a given sensitivity threshold. The designed GA evolved towards the selection of feature combinations which yielded high specificity in the high-sensitivity region of the ROC curve, regardless of the performance at low sensitivity. This is a desirable quality of a classifier used for breast lesion characterization, since the focus in breast lesion characterization is to diagnose correctly as many benign lesions as possible without missing malignancies. The high-sensitivity classifier, formulated as the Fisher's linear discriminant using GA-selected feature variables, was employed to classify 255 biopsy-proven mammographic masses as malignant or benign. The mammograms were digitized at a pixel size of mm, and regions of interest (ROIs) containing the biopsied masses were extracted by an experienced radiologist. A recently developed image transformation technique, referred to as the rubber-band straightening transform, was applied to the ROIs. Texture features extracted from the spatial grey-level dependence and run-length statistics matrices of the transformed ROIs were used to distinguish malignant and benign masses. The classification accuracy of the high-sensitivity classifier was compared with that of linear discriminant analysis with stepwise feature selection . With proper GA training, the ROC partial area of the high-sensitivity classifier above a true-positive fraction of 0.95 was significantly larger than that of , although the latter provided a higher total area under the ROC curve. By setting an appropriate decision threshold, the high-sensitivity classifier and correctly identified 61% and 34% of the benign masses respectively without missing any malignant masses. Our results show that the choice of the feature selection technique is important in computer-aided diagnosis, and that the GA may be a useful tool for designing classifiers for lesion characterization.en_US
dc.format.extent3118 bytes
dc.format.extent361393 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherIOP Publishing Ltden_US
dc.titleDesign of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosisen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPhysicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.identifier.pmid9814523en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/48962/2/m81014.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1088/0031-9155/43/10/014en_US
dc.identifier.sourcePhysics in Medicine and Biology.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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