Micro nucleus detection in human lymphocytes using convolutional neural network

Authors:
Ihor Paliy;Francesco Lamonaca;Volodymyr Turchenko;Domenico Grimaldi;Anatoly Sachenko
Affiliations:
Research Institute of Intelligent Computer Systems, Ternopil National Economic University, Ternopil, Ukraine;Department of Electronics, Informatics and Systems, University of Calabria, Rende, CS, Italy;Department of Electronics, Informatics and Systems, University of Calabria, Rende, CS, Italy;Department of Electronics, Informatics and Systems, University of Calabria, Rende, CS, Italy;Research Institute of Intelligent Computer Systems, Ternopil National Economic University, Ternopil, Ukraine
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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Abstract

The application of the convolution neural network for detection of the micro nucleuses in the human lymphocyte images acquired by the image flow cytometer is considered in this paper. The existing method of detection, called IMAQ Match Pattern, is described and its limitations concerning zoom factors are analyzed. The training algorithm of the convolution neural network and the detection procedure were described. The performance of both detection methods, convolution neural network and IMAQ Match Pattern, were researched. Our results show that the convolution neural network overcomes the IMAQ Match Pattern in terms of improvement of detection rate and decreasing the numbers of false alarms.