What is it about?
Novel computational methods have been developed to assist cervical cancer diagnosis, but the relationship between the digital information of the colposcopy test and histopathology data has not been completely studied. This paper shows the comparison of three well-known computational methods to try to discriminate among different kind of tissues to improve colposcopy test considering histopathologic data.
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Why is it important?
Our findings show there is a relation between computational representation of a reaction that happens during colposcopy and histopathologic data, this information can be used when physicians do not have a histopathological laboratory or they need to obtain a representative biopsy guided by colposcopy based on a computational representation.
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This page is a summary of: Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy, Computational and Mathematical Methods in Medicine, January 2017, Hindawi Publishing Corporation,
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