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Neural Computing and Applications

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01.09.2012 | Original Article

Investigation of different neural models for blood cell type identification

verfasst von: Adnan Khashman

Erschienen in: Neural Computing and Applications | Ausgabe 6/2012

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Abstract

The analysis of blood cells in microscope images can provide useful information concerning the health of patients; however, manual classification of blood cells is time-consuming and susceptible to error due to the different morphological features of the cells. Therefore, a fast and automated method for identifying the different blood cells is required. In this paper, we investigate the use of different neural network models for the purpose of cell identification. The neural models are based on the back propagation learning algorithm and differ in design according to the way data features are extracted from the cell microscopic images. Three different topologies of neural networks are investigated, and a comparison between these models is drawn. Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes, and orientation.

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Titel: Investigation of different neural models for blood cell type identification
verfasst von: Adnan Khashman
Publikationsdatum: 01.09.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0476-3

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