Abstract
We present a fuzzy fusion approach for combining cell-phase identification results obtained from multiple classifiers. This approach can improve the classification rates and allows the task of high-content cell-cycle screening more effective for biomedical research in the study of structures and functions of cells and molecules. Conventionally such study requires the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poorly reproducible. The proposed method has been used to combine the results from three classifiers, and the combined result is superior to any of the results obtained from a single classifier.
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Pham, T.D., Tran, D.T. (2006). Image Classification by Fusion for High-Content Cell-Cycle Screening. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_64
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DOI: https://doi.org/10.1007/11892960_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
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