Distinguishing Leukemic Cells Using Fractal Chromatin Patterns and Machine Learning

Authors

Abigail Gordhamer, Brigham Young University - ProvoFollow
Ryan Cordner, Brigham Young University - ProvoFollow
Paul Young, Brigham Young University - Provo

Files

Keywords

Hematology, Leukemia, Medical Lab Science, White blood cells, machine learning

Abstract

One of the most important tests in the clinical laboratory is the Complete Blood Count, which involves identifying the white blood cells in a patient’s blood. The respective counts of the different white blood cell types correlate with various states of health and disease and are critical to diagnosing diseases such as leukemia. Leukemic cells (blasts) are considered especially difficult to distinguish, and it is of the upmost importance that these cells are identified correctly. To aid in the process of leukemic cell identification, we quantified fractal patterns in the chromatin of white blood cells and used the data to identify cells with a random forest algorithm. By distinguishing between cells with the help of a machine learning algorithm, we hope to improve accuracy and efficiency in the clinical laboratory and more easily identify leukemic cells.

BYU ScholarsArchive Citation

Gordhamer, Abigail; Cordner, Ryan; and Young, Paul, "Distinguishing Leukemic Cells Using Fractal Chromatin Patterns and Machine Learning" (2024). Library/Life Sciences Undergraduate Poster Competition 2024. 40.
https://scholarsarchive.byu.edu/library_studentposters_2024/40

Document Type

Poster

Publication Date

2024-03-21

Language

English

College

Life Sciences

Department

Microbiology and Molecular Biology

University Standing at Time of Publication

Senior

Copyright Use Information

https://lib.byu.edu/about/copyright/