Abstract

One of the important medical test to evaluate overall human health condition is the Complete Blood Cell Count (CBC). Traditionally these blood cells were counted manually by visual inspection or by using the haemocytometer along with some chemical compounds and other scientific equipment’s which is a tedious and more time consuming task. To avoid this problem, the proposed work here is the machine learning approach of automatic identification and counting of blood cells (RBC’s, WBC’s and Platelets) using ‘You Only Look Once’ in short YOLO object detection and classification algorithm to automatically identify and count the blood cells from the blood smear images. This YOLO framework will be trained the modified Blood Cell Count Data set (BCCD) of blood smear images to automatically identify and count the RBS’s, WBC’s and Platelets from the blood smear images. Thus overall the computer aided system of counting and detection enables to count the blood cells just in less than a second and this can be useful in the practical applications.

Description