Blood cell identification using neural networks

The objective of this project is to propose a method of identifying three major blood cell types namely erythrocytes, leukocytes and platelets and to classify them based upon their morphological features using neural networks. The data are collected using peripheral blood smears from clinical patients. The image acquisition requires 100/spl times/ magnification on all the blood smears, the preprocessing involves the use of median and edge enhancing filters and 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.