ref.bib

@article{akramifard_extracting_2012,
	title = {Extracting, Recognizing, and Counting White Blood Cells from Microscopic Images by Using Complex-valued Neural Networks},
	volume = {2},
	issn = {2228-7477},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660713/},
	abstract = {In this paper a method related to extracting white blood cells ({WBCs}) from blood microscopic images and recognizing them and counting each kind of {WBCs} is presented. In medical science diagnosis by check the number of {WBCs} and compared with normal number of them is a new challenge and in this context has been discussed it. After reviewing the methods of extracting {WBCs} from hematology images, because of high applicability of artificial neural networks ({ANNs}) in classification we decided to use this effective method to classify {WBCs}, and because of high speed and stable convergence of complex-valued neural networks ({CVNNs}) compare to the real one, we used them to classification purpose. In the method that will be introduced, first the white blood cells are extracted by {RGB} color system's help. In continuance, by using the features of each kind of globules and their color scheme, a normalized feature vector is extracted, and for classifying, it is sent to a complex-valued back-propagation neural network. And at last, the results are sent to the output in the shape of the quantity of each of white blood cells. Despite the low quality of the used images, our method has high accuracy in 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.},
	pages = {169--175},
	number = {3},
	journaltitle = {Journal of Medical Signals and Sensors},
	shortjournal = {J Med Signals Sens},
	author = {Akramifard, Hamid and Firouzmand, Mohammad and Moghadam, Reza Askari},
	urldate = {2019-04-01},
	date = {2012},
	pmid = {23717809},
	pmcid = {PMC3660713}
}

@article{SMOalgo,
	title = {Extracting, Recognizing, and Counting White Blood Cells from Microscopic Images by Using Complex-valued Neural Networks},
	volume = {2},
	issn = {2228-7477},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660713/},
	abstract = {In this paper a method related to extracting white blood cells ({WBCs}) from blood microscopic images and recognizing them and counting each kind of {WBCs} is presented. In medical science diagnosis by check the number of {WBCs} and compared with normal number of them is a new challenge and in this context has been discussed it. After reviewing the methods of extracting {WBCs} from hematology images, because of high applicability of artificial neural networks ({ANNs}) in classification we decided to use this effective method to classify {WBCs}, and because of high speed and stable convergence of complex-valued neural networks ({CVNNs}) compare to the real one, we used them to classification purpose. In the method that will be introduced, first the white blood cells are extracted by {RGB} color system's help. In continuance, by using the features of each kind of globules and their color scheme, a normalized feature vector is extracted, and for classifying, it is sent to a complex-valued back-propagation neural network. And at last, the results are sent to the output in the shape of the quantity of each of white blood cells. Despite the low quality of the used images, our method has high accuracy in 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.},
	pages = {169--175},
	number = {3},
	journaltitle = {Journal of Medical Signals and Sensors},
	shortjournal = {J Med Signals Sens},
	author = {Akramifard, Hamid and Firouzmand, Mohammad and Moghadam, Reza Askari},
	urldate = {2019-04-01},
	date = {2012},
	pmid = {23717809},
	pmcid = {PMC3660713}
}