White blood cell subtype detection and classification

TL;DR

This paper proposes using YOLOv3 for simultaneous localization and classification of white blood cells in blood images, achieving high accuracy and reducing diagnostic errors and time.

Contribution

It introduces a novel application of YOLOv3 for white blood cell detection and classification, improving speed and accuracy over traditional methods.

Findings

White blood cell detection accuracy of 99.2%

Classification accuracy of 90%

Effective localization and classification in blood images

Abstract

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.