extracting, recognizing, and counting White blood cells from microscopic images by using complex-valued neural networks

In this paper a method related to extracting white blood cells (wbcs) from blood microscopic images and recognizing them and countingeach kind of wbcs is presented. in medical science diagnosis by check the number of wbcs and compared with normal number ofthem is a new challenge and in this context has been discussed it. after reviewing the methods of extracting wbcs from hematologyimages, because of high applicability of artificial neural networks (anns) in classification we decided to use this effective method toclassify wbcs, and because of high speed and stable convergence of complex-valued neural networks (cvnns) compare to the realone, we used them to classification purpose. in the method that will be introduced, first the white blood cells are extracted by rgbcolor system’s help. in continuance, by using the features of each kind of globules and their color scheme, a normalized feature vectoris extracted, and for classifying, it is sent to a complex-valued back-propagation neural network. and at last, the results are sent to theoutput in the shape of the quantity of each of white blood cells. despite the low quality of the used images, our method has high accuracyin extracting and recognizing wbcs by cvnns, and because of this, certainly its result on high quality images will be acceptable.learning time of complex-valued neural networks, that are used here, was significantly less than real-valued neural networks.