title: Neural networks and blood cell identification.
abstract: The objective of this project is to propose a method of identifying cells found in human blood and to classify them based upon their morphological features using neural networks. The project focuses on three major blood cell types, namely, erythrocytes, leukocytes and platelets. The data are collected using peripheral blood smears from clinical patients. The image acquisition requires 100x magnification on all the blood smears, the preprocessing involves the use of median and edge enhance filters; the feature extraction is done by performing the wavelet transform on the images. Finally classification of the blood cell types is done using ALOPEX and Back Propagation trained neural networks. The efficacy of both networks is then compared by comparing their outputs and number of iterations required to reach the final result.
authors.affiliation: Rutgers University, Department of Biomedical Engineering, Piscataway, New Jersey 08855-0909, USA.
authors.forename: E
authors.initials: E
authors.lastname: Micheli-Tzanakou
authors.forename: H
authors.initials: H
authors.lastname: Sheikh
authors.forename: B
authors.initials: B
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001773:Blood Cells / Q000145:classification* / Q000166:cytology / Q000648:ultrastructure; D001792:Blood Platelets / Q000145:classification / Q000166:cytology / Q000648:ultrastructure; D002467:Cell Nucleus / Q000648:ultrastructure; D048429:Cell Size; D004912:Erythrocytes / Q000145:classification / Q000166:cytology / Q000648:ultrastructure; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007962:Leukocytes / Q000145:classification / Q000166:cytology / Q000648:ultrastructure; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated