Blood cell identification using a simple neural network.
Classification of blood cell types can be time consuming and susceptible to error due to the different morphological features of the cells. This paper presents a blood cell identification system that simulates a human visual inspection and identification of the three blood cell types. The proposed system uses global pattern averaging to extract cell features, and a neural network to classify the cell type. Two neural networks are investigated and a comparison between these networks is drawn. Experimental results suggest that the proposed system provides fast, simple and efficient identification which can be used in automating laboratory reporting.
Khashman
Adnan
A
Electrical & Electronic Engineering Department, Near East University Lefkosa, Mersin, Turkey. amk@neu.edu.tr
D000465:Algorithms; D001185:Artificial Intelligence; D001772:Blood Cell Count / Q000379:methods*; D001792:Blood Platelets / Q000166:cytology; D004912:Erythrocytes / Q000166:cytology; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D007962:Leukocytes / Q000166:cytology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D012986:Software Validation