Abstract
Blood cell detection can be considered as a gold standard key in diagnosing blood disease and can produce an automatic report to hematologists and doctors. Blood cell detection can considera challenging task due to high number of overlapped cells per image, non-illumination level, the variety of staining process, and variations in cell densities among platelets, white blood cells and red blood cells. Traditional procedure of blood cell detection requires pathologist effort and time. In computer aided diagnosissystem, machine learning and deep learning techniques become the practical way to automate the procedure of diagnosing, classify microscopic blood cells,and increase the accuracy and speed of the procedure. This paper provides a review of theblood celldetection and classificationprocess, including white blood cells, red blood cells, and platelets and their characteristics using machine learning techniques. We also have detailed the dataset of microscope blood cell.The previous works have been divided into four categories based on the models, output,including pre-processing, segmentation,feature extractionandclassification. Then, we discuss the challenges that face thesemethods and suggest the potential future techniques
Recommended Citation
Makki, Teba Mazin and Dulaimi, Khamael Al
(2023)
"Blood Cell Microscopic Image Classification in ComputerAided Diagnosis Using Machine Learning: A Review,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
2, Article 4.
DOI: https://doi.org/10.52866/ijcsm.2023.02.02.002
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss2/4
