Classification of White Blood Cells Using Convolutional Neural Network
Amin Edraki, Abolhassan Razminia · Iranian South Medical Journal · 2018
Background: Observing, categorizing and counting various types of white blood cells in a blood sample is one of the most important steps in the treatment of various diseases.This study aimed to develop a fast and reliable system based on processing microscopic images of blood samples for classifying four types of white blood cells. Materials and Methods:The modified k-means clustering method was used to perform image segmentation.Furthermore, white blood cells were classified using a deep convolutional neural network with the help of data in the MISP database, a free database composed of microscopic blood sample images.Moreover, several regularization techniques such as dropout and image augmentation were applied to prevent overfitting of the network. Results:The classification accuracy of the neural network was found to be 99%, which is more successful than many earlier studies.In the segmentation section, the cross-reference index was 0.73.Conclusion: The results of this research show that processing the microscopic images of the blood sample can help develop rapid and reliable systems using different methods of image processing and machine learning.