Abstract: With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed forward back propagation neural network. After segmentation of blood cells that are obtained from microscopic images, the most 16 significant features of these cells are fed as inputs to the neural network. Half of the 100 of the WBC sub-images that are found after segmentation are used to train the neural network, while the other half is used for test. The results found are promising with classification accuracy being 96%.

Mazin Z. Othman, Thabit S. Mohammed and Alaa B. Ali, “Neural Network Classification of White Blood Cell using Microscopic Images” International Journal of Advanced Computer Science and Applications(IJACSA), 8(5), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080513

@article{Othman2017,
title = {Neural Network Classification of White Blood Cell using Microscopic Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080513},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080513},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {5},
author = {Mazin Z. Othman and Thabit S. Mohammed and Alaa B. Ali}
}