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Cancer detection using neural computing methodology

Show simple item record Toomarian, Nikzad Kohen, Hamid S. Bearman, Gregory H. Seligson, David B. 2007-09-10T23:00:41Z 2007-09-10T23:00:41Z 2001-10-20
dc.identifier.citation 13th European Simulation Symposium (ESS), Marseilles, France, October 18-20, 2001 en
dc.identifier.clearanceno 01-1856
dc.description.abstract This paper describes a novel learning methodology used to analyze bio-materials. The premise of this research is to help pathologists quickly identify anomalous cells in a cost efficient method. Skilled pathologists must methodically, efficiently and carefully analyze manually histopathologic materials for the presence, amount and degree of malignancy and/or other disease states. The prolonged attention required to accomplish this task induces fatigue that may result in a higher rate of diagnostic errors. In addition, automated image analysis systems to date lack a sufficiently intelligent means of identifying even the most general regions of interest in tissue based studies and this shortfall greatly limits their utility. An intelligent data understanding system that could quickly and accurately identify diseased tissues and/or could choose regions of interest would be expected to increase the accuracy of diagnosis and usher in truly automated tissue based image analysis. en
dc.description.sponsorship NASA/JPL en
dc.format.extent 1874593 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2001 en
dc.subject cancers en
dc.subject neural network learning en
dc.subject morphology en
dc.subject molecular assessments en
dc.subject multi-spectral images en
dc.subject liquid crystal tunable filters en
dc.title Cancer detection using neural computing methodology en
dc.type Preprint en

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