  Machine learning has endless applications in the health care industry. White
blood cell classification is one of the interesting and promising area of
research. The classification of the white blood cells plays an important part
in the medical diagnosis. In practise white blood cell classification is
performed by the haematologist by taking a small smear of blood and careful
examination under the microscope. The current procedures to identify the white
blood cell subtype is more time taking and error-prone. The computer aided
detection and diagnosis of the white blood cells tend to avoid the human error
and reduce the time taken to classify the white blood cells. In the recent
years several deep learning approaches have been developed in the context of
classification of the white blood cells that are able to identify but are
unable to localize the positions of white blood cells in the blood cell image.
Following this, the present research proposes to utilize YOLOv3 object
detection technique to localize and classify the white blood cells with
bounding boxes. With exhaustive experimental analysis, the proposed work is
found to detect the white blood cell with 99.2% accuracy and classify with 90%
accuracy.

Computer Vision and Pattern Recognition