White blood cell subtype detection and classification
This addresses a domain-specific issue for healthcare by providing a computer-aided system to improve efficiency and accuracy in white blood cell analysis, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of automating white blood cell subtype detection and classification to reduce time and human error in medical diagnosis, achieving 99.2% detection accuracy and 90% classification accuracy using YOLOv3.
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.