Abstract
The automation of chromosome identification and visualization for a complete cell (karyotyping) has been the subject of considerable research. While rather high classification rates are possible on individual chromosomes, the cell level classification rates are still quite low. We describe a system which uses partial confidence values generated by neural and fuzzy classifiers with optimization to increase the cell level recognition rates. This is consistent with Marr's Principle of Least Commitment for the design of intelligent computer vision algorithms.
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