Classification of White Blood Cells in Peripheral Blood Sample using Convolutional Neural Network

Background: Observation, categorize and count various types of white blood cells in a blood sample is a One of the most important steps in the treatment of various diseases. The aim of this study was to design and implement a fast and reliable and based on the processing of microscopic images of blood samples for the classification of four types of white blood cells. Materials and Methods: In this article, the modified k-means clustering method is used to perform image segmentation. Furthermore, The classification of white blood cells was done using a deep convolutional neural network and 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 the network from overfitting. Results: In the classification category, the accuracy of the neural network is measured to be 99%, which has been 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 rapid and reliable system design and implementation is possible by processing the microscopic images of the blood sample using different methods of image processing and machine learning